Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to compute the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation. Accurately predicting complex traits and diseases  based on an individual’s genetic variants is crucial for effective disease prevention and personalized treatment -8. The ghttps://zzz.bwh.harvard.edu/plink/) ) from the index SNPs  and that are in LD with the index SNP based on the r2 threshold . For p-value thresholding, the SNPs are generated with p-values less than a provided threshold (PT) and then candidate PRSs corresponding to the thresholds are created with the PLINK options . The best PRS is selected among candidate PRSs computed at a range of p-value thresholds based on the prediction R2. For the automation of the C+T approach in PLINK, we can utilize PRSice and PRSice-2, which are options in R software for computing and evaluating the PRS. PRSice and PRSice-2 are popular PRS tools and constitute efficient and scalable software for automating and simplifying PRS computation on large-scale GWAS data. They handle imputed data as well as genotyped data and simultaneously evaluate a large number of continuous and binary phenotypes. Similar to PLINK, they require summary data as well as phenotype, covariate, and genotype data for the target samples. They automate the procedure of the standard C+T method, which utilizes PLINK options for PRS analysis.Popular genetic tools, such as PLINK , PRSice e.info/) , and PRSe.info/) , are utiA critical issue in estimating the PRS is the LD structure between SNPs, which has been heuristically addressed by LD pruning and LD clumping. Recently, LDpred was developed as a more sophisticated method that also utilizes summary statistics . It has i, jβ is the effect size for SNP j, ijx is the genotype for sample i and SNP j, and j.LDpred is an LD matrix and summary statistics–based Bayesian method for polygenic prediction, which is a popular tool for deriving the PRS . It compj, jβ~0 with probability 1-p, where p is the proportion of causal SNPs. The posterior mean effect size is estimated as jth SNP is causal, which can be interpreted as non-uniform shrinking of the estimated effect size In the special case of no LD between SNPs, the posterior mean can be computed analytically. Under a Gaussian infinitesimal prior, D is an LD matrix (M×M) that needs to be estimated by the LD in a reference panel (LDpred-inf). LDpred-inf is a natural extension of the GBLUP to summary statistics. Under a Gaussian non-infinitesimal prior, posterior means cannot be computed analytically but they can be computed with Markov-chain Monte Carlo Gibbs samplers. First, jβ values are initialized based on an infinitesimal prior with LD (LDpred-inf). At each iteration, jβ is resampled from f(jβ) reflects the point-normal prior  with at least 1,000 individuals of the same ancestry as the individuals for summary statistics. The second step generates an LD information file with a pre-specified LD radius and re-weights the SNP effects with the ‘ldpred gibbs’ command. One LD information file is created with a pre-specified LD radius, but several SNP weight files are generated corresponding to the different values of p . The third step computes the PRS for individuals in the target dataset with the ‘ldpred score’ command. Separate PRS files are generated corresponding to the different values of p. Additionally, LDpred provides a pruning and thresholding option as an alternative method with the ‘ldpred p+t’ command. This option often yields better prediction results than the original LDpred when the sample size of LD reference panel is not large enough.The procedure for computing the PRS using LDpred consists of three steps: (1) synchronizing the genotype and summary data, (2) generating LDpred SNP weights, and (3) generating the individual PRS. The first step synchronizes genotype and summary statistics and then generates the coordinated genotype data with the ‘The construction of a genome-wide PRS using LDpred requires summary statistics from existing large-scale GWAS studies  the ‘auto’ option, which learns the tuning parameter p, which is the proportion of causal SNPs, directly from the dataset. LDpred-2 was implemented in the R package ‘bigsnp’.Recently, LDpred-2, a new version of LDpred, was developed to improve predictive performance compared to LDpred . It provAn alternative to summary-based approaches is to fit the effect sizes of all SNPs simultaneously using BLUP models, which is a more traditional approach for computing the PRS. Fitting all SNPs simultaneously is more appropriate than summary-based approaches, producing more accurate predictors.y=Xβ+g+e, where y is a vector of phenotypes (N×1), X is a matrix of covariates excluding the SNPs (N×C), β is a vector of covariate effects (C×1) and g is a vector of random genetic effects for all individuals with N×1) (A is a N×N genetic related matrix [GRM]) and e is a vector of random errors with N×1). The genetic values  are estimated as N×N matrix. A GBLUP model can be transformed to a ridge regression BLUP model (RRBLUP) [y=Xβ+Wu+e, where W is a matrix of standardized genotypes (N×M) and u is a vector of random SNP effects with M×1). The SNP effects  are estimated as A and individual BLUP newW is a matrix of standardized genotypes in the target dataset, GBLUP methods utilize individual-level GWAS data, not summary statistics, to estimate SNP effects using LMMs. The GBLUP model is (RRBLUP) ,44, whicV is a matrix of standardized genotypes from the reference panel, and tn and rn are the sample sizes for the training and reference samples, respectively. This assumes the similarity of allele frequencies and LD structure between training and reference samples. It also approximates The GBLUP models require individual-level genotype and phenotype data for training, but this is not always possible. Instead, summary SBLUP models can be utilized by approximating individual-level genotype and phenotype data using summary statistics and a reference panel . The SBLA) is first estimated from the training genotype data with the ‘--make-grm’ option, and then the individual BLUP , X is a matrix of standardized genotypes (N×M), β is a vector of SNP effects (M×1) and e is a vector of random errors with N×1). It also assumes the SNP effects result from a finite normal mixture of C components, so that the prior for β becomes π= and β is β is sampled using the Gibbs sampling scheme. The posterior mean for SNP effects  to estimates of regression coefficients from M simple linear regression (b) by multiplying y=Xβ+e by D-1TX, where D-1TX)y=(D-1TX)Xβ+(D-1TX)e. Noting that b=D-1TXy is the vector of the least squares marginal regression effects estimates and D is replaced by the estimates B is replaced by β is C denotes the pre-specified maximum number of components in the finite mixture model, π= and γ=. The default values are C=4, γ=. The posterior for β is β, are sampled using the Gibbs sampling approach and the posterior mean for the SNP effects  is a software tool that contains a family of Bayesian LMMs for complex trait analyses using GWAS SNPs. First of all, GCTB specifies the Bayesian alphabet for the analysis with the option ‘--bayes': R for BayesR. The options ‘--pi 0.05’  and ‘--hsq 0.5’ , ‘--gamma 0,0.01,0.1,1’ (a prespecified hyperparameter γ), and ‘--gwas-summary’ (an input file for GWAS summary statistics).GCTB , X is a matrix of genotypes (N×M), β is a vector of SNP effects (M×1) and e is random error with N×1). The elastic net regression obtains the estimates of β by minimizing the following object function: L1 norm of β, L2 norm of β, and λ and α are tuning parameters to be estimated. When α=1,f(β) becomes the object function for lasso regression, and when α=0, it becomes the object function for ridge regression. The PRSs in target samples are constructed with the estimated SNP effects from the lasso or elastic net and genotype data from the target dataset.Penalized regression methods such as the lasso ,19, the r=TyX is the SNP-wise correlation between the SNPs and the phenotype and R=TXX is the LD matrix, a matrix of correlations between SNPs. The lassosum approximates R by s<1 where rX is matrix of genotypes from a reference panel and also approximates r by obtaining publicly available summary statistics. The lassosum constructs PRSs using summary statistics and a reference panel in a penalized regression setting.The lassosum is a method for computing lasso or elastic net estimates using GWAS summary statistics and an LD reference panel . The objhttps://cran.r-project.org/web/packages/glmnet/). The ‘glmnet’ package fits a generalized linear model via the penalized maximum likelihood approach. It is not originally designed for GWAS studies, but it is widely used for PRS analyses due to its computational efficiency. The lassosum is a R package or standalone software for performing lasso or elastic net regression with summary data and an LD reference panel. The reference panel is assumed to be in the PLINK format, and the GWAS summary statistics are given as data.frame in R.The most popular tool for lasso, ridge, and elastic net regression is ‘glmnet’ in R : iy=iXiβ+ig+ie=iXiβ+iWiu+ie (i.e. ig=iWiu) where i=1,…,T. The individual BLUP model T traits are given as:In order to utilize multiple traits to improve prediction accuracy, the RRBLUP and GBLUP methods are extended to the bivariate ridge regression method  and MTGBnewWnewW is a matrix of standardized genotypes in the target dataset and The individual BLUP in a validation sample T traits can be re-written as L is an M×M scaled LD correlation matrix estimated from a reference panel and T traits can be approximately computed as The wMT-SBLUP  creates RSS(β) is the residual sum of squares, λ1 using lasso or the minimax concave penalty to induce a sparse solution, and λ2 to incorporate shared genetic effects across multiple traits for large-sample GWAS data. It induces smoothness of the coefficients and can incorporate prior knowledge on the similarity of a pair of traits at a given SNP via adjacency coefficients. It also incorporates multiple secondary traits based on individual-level genotypes and/or summary statistics. The PRS in target samples is computed as tX is a matrix of standardized genotypes in the target dataset, To utilize multiple traits for PRS computation, the CTPR method was developed . The SNPGenetic risk prediction in diverse populations currently lags far behind risk prediction in European samples ,53. StriWe have reviewed statistical models and computational tools for PRS computation. We have demonstrated a variety of statistical models for genomic risk prediction using individual-level data and/or summary statistics and showed how to improve prediction accuracy with multiple traits and multiple populations. Furthermore, we have introduced recent computational tools to conduct PRS analyses based on the statistical models, and explained how to specify the parameters and how to execute the software in detail. We also summarized which statistical models and software are best for specific situations based on data type , sample size, the LD reference panel, the number of traits, and the number of ethnicities, as shown in Despite the existence of various PRS methods, there are some areas in which further research on PRS is required. To improve prediction accuracy, we need novel statistical models and software that leverage information from multiple disease outcomes and multiple ethnicities based on individual-level genotype data and/or summary statistics from large-scale biobanks. It is also necessary to develop methods with the ability to predict diverse disease traits, such as cardiovascular disease and type 2 diabetes, with sufficient accuracy , and then these models need to be extended to utilize multiple ethnicities by incorporating information on LD to further improve prediction accuracy.Moreover, with advances in high-throughput molecular assays , it has been shown that disease risk SNPs are enriched in a broad array of functional regions, including regulatory features that are often tissue-specific, providing a novel source of information for improved prediction accuracy. It has been further shown that these molecular features can be predicted from genetic variants, enabling the prediction of gene expression in GWAS cohorts to perform transcriptome-wide association studies and to identify putative susceptibility genes. The accurate prediction of individual molecular features is now an emerging tool for discovering novel disease loci and characterizing biological mechanisms at the thousands of GWAS loci that have already been published. Data collection efforts of an unprecedented scale are now being seen in the areas of functional genomics and disease genetics. Such datasets can help to prioritize causal features and further improve prediction accuracy.We conclude by emphasizing the importance of creating accurate PRS for a wide range of complex traits and diseases. The PRS provides an estimate of genetic predisposition  for a complex trait or disease at the individual level, which refers to the likelihood of developing a particular trait or disease based on a genotype profile. The goal of PRS analysis is to identify individuals at an elevated risk of diseases on the basis of genetic variants in combination with clinical covariates. Therefore, the more accurate PRS we obtain, the better we can identify disease risk and the better we can provide treatment and prevention strategies. Personalized medicine based on accurate PRS will have a considerable impact on the treatment process and quality of life in the near future.


Using autism spectrum disorder summary statistics from the Psychiatric Genomic Consortium , we applied polygenic risk score analyses to understand the genetic relationship between autism spectrum disorders and autistic-like traits. Using MAGMA, we performed gene-based and gene co-expression network analyses to delineate involved genes and pathways. We identified two novel genome-wide significant loci – rs6125844 and rs3731197 – associated with ‘attention-to-detail’. We demonstrated shared genetic aetiology between autism spectrum disorders and ‘rigidity’. Analysing top variants and genes, we demonstrated a role of the immune-related genes RNF114, CDKN2A, KAZN, SPATA2 and ZNF816A in autistic-like traits. Brain-based genetic expression analyses further linked autistic-like traits to genes involved in immune functioning, and neuronal and synaptic signalling. Overall, our findings highlight the potential of the autistic-like trait–based approach to address the challenges of genetic research in autism spectrum disorders. We provide novel insights showing a potential role of the immune system in specific autism spectrum disorder dimensions.The clinical heterogeneity of autism spectrum disorders majorly challenges their genetic study. Autism spectrum disorders symptoms occur in milder forms in the general population, as autistic-like traits, and share genetic factors with autism spectrum disorders. Here, we investigate the genetics of individual autistic-like traits to improve our understanding of autism spectrum disorders. We meta-analysed four population-based genome-wide association studies investigating four autistic-like traits – ‘attention-to-detail’, ‘imagination’, ‘rigidity’ and ‘social-skills’ (Autism spectrum disorders are complex, with a strong genetic basis. Genetic research in autism spectrum disorders is limited by the fact that these disorders are largely heterogeneous so that patients are variable in their clinical presentations. To address this limitation, we investigated the genetics of individual dimensions of the autism spectrum disorder phenotypes, or autistic-like traits. These autistic-like traits are continuous variations in autistic behaviours that occur in the general population. Therefore, we meta-analysed data from four different population cohorts in which autistic-like traits were measured. We performed a set of genetic analyses to identify common variants for autistic-like traits, understand how these variants related to autism spectrum disorders, and how they contribute to neurobiological processes. Our results showed genetic associations with specific autistic-like traits and a link to the immune system. We offer an example of the potential to use a dimensional approach when dealing with heterogeneous, complex disorder like autism spectrum disorder. Decomposing the complex autism spectrum disorder phenotype in its core features can inform on the specific biology of these features which is likely to account to clinical variability in patients. Autism spectrum disorders (ASDs) refer to a class of common and pervasive conditions with an early life onset . Core AS2~70%–90%), demonstrating the importance of genetic research on these conditions , lacking power to detect robust genome-wide associations  is a population-based study set up by the Department of Health Evidence and the Department of Laboratory Medicine of the Radboud University Medical Center (Radboudumc) in Nijmegen, The Netherlands. The NBS investigates the role of genetic and environmental factors on individual well-being  project is an initiative promoted by the Human Genetics Department of the Radboudumc, the Donders Centre for Cognitive Neuroimaging of the Radboud University, and the Max Planck Institute for Psycholinguistics in Nijmegen, The Netherlands. The BIG project investigates genetic variation linked to behaviour, cognition, brain structure and function in the general population  is a large prospective cohort study of pregnancy, childhood, adolescence and adulthood based in Western Australia. The study investigates the role of genetic and environmental factors on individual well-being using a longitudinal approach (The Raine Study (approach . The RaiGenetics of Cognition (GenofCog) is a general population study set up by the Turner Institute of Brain and Mental Health and the Monash University in Melbourne, Australia. The study investigates genetic variations and neural correlates related to cognition and psychopathology . The stuSupplementary Table 2. Individual ALT scores were calculated, and outliers (2 standard deviations (2SD) from the mean) removed (see The Autism Spectrum Quotient (AQ)  and a cu−6) and minor allele frequency (MAF >0.01). In each cohort, genotyped data for the autosomal chromosomes were imputed to increase genotype density and achieve fine genome-wide mapping. For all data sets, imputation followed the ENIGMA protocol (http://enigma.ini.usc.edu/wp-content/uploads/2012/07/ENIGMA2_1KGP_cookbook_v3.pdf), that adopts the 1000 Human Genome Project reference panel and the software MACH  divided by the ALTs tested. In order to estimate the statistical power we had to find genome-wide significantly associated SNPs in our meta-analysis of 4600 individuals, we performed a power calculation in QUANTO  throughout the genome were considered. Next, PRSs were calculated on the clumped ASD summary statistics and included only SNPs exceeding seven broad p-value thresholds . For each threshold, ASD-PRS was extracted and used to estimate the extent of shared genetic aetiology with ALTs. p-values were corrected using the False Discovery Rate (FDR) method in R.Polygenic risk score (PRS)–based analyses were applied to estimate the extent of shared, common variant, genetic aetiology between clinical ASDs and ALTs using PRSice (v1.25) . To set Z-statistic was obtained considering the p-values of gene-related SNPs, while correcting for LD. We applied the significance p-value threshold p = 2.8-6, accounting for the total number of genes tested.Gene-wide analyses on ALT-GWAS results were performed to identify ALT-associated genes using the Multi-marker Analysis of GenoMic Annotation (MAGMA) software . First, https://github.com/eskederks/eMAGMA-tutorial. Accordingly, we mapped SNPs to genes based on available annotation files, that were tissue-specific and referred to significant (FDR <0.05) SNP-gene associations from GTEx . Next, we performed gene-wide analyses to link ALT-GWAS SNPs to eQTL-associated gene (eGenes) using annotation files for seven ASD-associated brain regions   of correlated genes. Using the MAGMA gene-set approach, we performed a competitive test testing the association of each module with the ALTs. Results were then Bonferroni-corrected . Finally, we performed post hoc analyses on the significant gene-set associations to define the biological functions of the identified gene-expression modules. We used the g:GOst tool from the g:Profiler webserver (https://biit.cs.ut.ee/gprofiler/gost) (http://geneontology.org/). The g:SCS option was chosen to correct for multiple testing while controlling for the inter-correlation between GO terms.To explore the functional role of ALT-associated SNPs, we performed gene co-expression network analysis using the eQTL-MAGMA (e-MAGMA) software package . Analysemygdala) . Gene-wier/gost) , which pWe integrated brain-specific gene expression analyses for ALTs with gene expression analyses across a wider range of human tissues. To do so, we exploited the tool for functional mapping and annotation (FUMA) of GWAS that refers to tissue-specific expression patterns based on GTEx v6 RNA-seq data . We usedrs6125844 (p = 1.52 × 10−⁹) and rs3731197 (p = 1.9 × 10−⁸) (rs6125844-association survived our stringent correction (p < 1.25 × 10−9), whereas the rs3731197-association exceeded the genome-wide significance threshold (p < 5 × 10−8). No SNP-association reached significance for the other ALTs (Supplementary Informationsee ).ALT-based meta-analyses revealed genome-wide significant associations of ‘attention-to-detail’ with two SNPs,  × 10−⁸) . The rs6TP = 0.05, TP = 0.1 and TP = 0.5 (FDR-corrected p < 0.01). Considering the common genetic variants captured by our analyses, we did not find a statistically significant genetic sharing between ASDs and other ALTs (see Supplementary Information).Using the PRS-based approach, we found a shared genetic aetiology between clinical ASDs and ‘rigidity’ . The mosRNF114, CDKN2A, SPATA2, KAZN – were significantly associated with ‘attention-to-detail’. In addition, ZNF816 was significantly associated with ‘social skills’ , while nominally associated with expression-module for putamen (p = 0.01). Imagination-related eGenes were associated the expression-module for nucleus accumbens (p = 0.003) and amygdala (p = 0.001). Rigidity-related eGenes were nominally associated with modules for anterior cingulate cortex (p = 0.01) and nucleus accumbens (p = 0.01). Finally, social skill–related eGenes were associated with an expression-module for anterior cingulate cortex (p = 0.001) and putamen (p = 0.002); functional enrichment analyses revealed a expression-module enrichment for biological processes, including synaptic signalling, neurogenesis. Among the enriched pathways we also identified immune-related processes, such as cytokine signalling, adding support to the results of our gene-wide analyses of ALTs. Results of the e-MAGMA and enrichment ALT-analyses are presented in the Supplementary Information (Supplementary Table 1), as well as results from FUMA-based analyses of gene expression across multiple human tissues beyond brain . Additional data about GWASs results, PRS-based analyses and gene co-expression network analyses can be found in the Supplementary Information materials.Gene co-expression network analyses revealed ALT-specific associations with gene-expression modules across ASD-related brain regions. Namely, we observed that attention-to-detail eGenes were statistically associated with the expression-module for total cortex (rs6125844 and rs3731197) that were significantly associated with ‘attention-to-detail’. Our PRS-based analysis was significant for the comparison between ASDs and ‘rigidity’. Next, we showed significant associations between ‘attention-to-detail’ and RNF114, CDKN2A, SPATA2 and KAZN, and between ‘social-skills’ and ZNF816A. Finally, we demonstrated that ALT-eQTLs are associated with gene-networks in ASD-related brain regions. Biological characterisation of these gene-networks showed enrichment in synaptic signalling, neurogenesis and the immune response.In this study, we investigated the genetics of four ALTs – ‘attention-to-detail’, ‘imagination’, ‘rigidity’ and ‘social-skills’ – by meta-analysing GWAS data of 4600 individuals from the general population. We found two common genetic variants , that contribute to neuronal functions, like axon guidance and synaptic signalling which have been linked to ASD pathophysiology  synaptic signalling . HoweverMET gene as observed in only the NBS cohort. Also, although the levels of ALTs in our cohort have been checked for normality, we could not exclude the possibility that any individual received a formal ASD diagnosis. To this regard, we believe that future ALT-based research, adopting an ASD diagnosis as exclusion criteria and potentially even larger sample sizes, could help to validate our conclusions. Moreover, this study explored genetic variants associated with factors extracted from a validated ALT questionnaire. Our findings of genetic diversity between ALTs should therefore encourage and seek for replication in future ALT research relying on questionnaires developed for each of these ALTs.This study should be evaluated in the light of some strengths and limitations. First, trait-oriented research exploits population-based cohorts for which large data are accessible at little cost. Relying on population-based cohorts, we combined data from multiple sources worldwide to obtain a sample large enough to perform a well-powered genetic ALT-investigation. The resulting sample size was, indeed, increased with respect to previous GWASs of ALTs that counted ~2000 unrelated individuals. Second, the analysis of our top findings offered new insights into the biology of autistic-like behaviours, hinting to the immune response. This does not only indicate potential areas for future investigation, but it helps to clarify the molecular profile underlying diverse autistic dimensions. The aggregation of multiple cohorts, however, increased the variability of this study population. Differences in gender distribution exist in the cohorts used see . AlthougTP = 0.05, 0.1 and 0.5), we could not find significant association when considering SNPs at the most conservative threshold (TP = 0.001). This result may indicate a combined effect of a wide range of small-effect ASD-related SNPs in predicting ALT variability. However, further PRS-based analyses, involving larger samples, are needed to clarify the genetic relationship between ASDs and ALTs. To further clarify the ALT-ASD genetic relationship, it would be valuable to assess the extent of symptoms variance explained by each ALT’s genetics specifically within individuals diagnosed with ASD. Large patient-based studies would be needed to address this point in future studies.Our PRS-based analyses indicate a significant genetic association between polygenic risk scores for ASDs and ‘rigidity’; however, ASD-related variants could explain at most 0.20% of the genetic variance for ‘rigidity’. This result differs from previous a report that demonstrated a genetic correlation between population-based autistic traits and autistic formal diagnoses of ~0.50–0.60 . The polFinally, this study specifically investigated common genetic variations associated with population-based autistic traits. In light of the confirmed role of both common and rare variants in ASDs , we beliOur analyses revealed genetic loci linked to ALTs in the general population which may be of relevance for ASDs. Our data demonstrate genetic concordance between ALTs and clinical ASDs demonstrating the potential to use population-based ALTs to address the complex ASD genetics. ALT-associated SNPs and genes seem involved in the immune response and eQTL signals for different ALTs are enriched for immune-related processes in the brain. These findings suggest an immune-ALT link that should inform further investigation. Overall, research on disorder-related traits has the potential to parse the heterogeneity of disorders and highlight dimension-specific biological pathway also important for pharmacotherapy.Click here for additional data file.Supplemental material, sj-pdf-1-aut-10.1177_13623613211019547 for Potential role for immune-related genes in autism spectrum disorders: Evidence from genome-wide association meta-analysis of autistic traits by Martina Arenella, Gemma Cadby, Ward De Witte, Rachel M Jones, Andrew JO Whitehouse, Eric K Moses, Alex Fornito, Mark A Bellgrove, Ziarih Hawi, Beth Johnson, Jeggan Tiego, Jan K Buitelaar, Lambertus A Kiemeney, Geert Poelmans and Janita Bralten in Autism


MGAT5, ST3GAL4, B3GAT1; IgG - MGAT3, ST6GAL1), as well as shared associations . 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.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 -  Post-translational modifications are known to diversify protein functions, but the effect of genetic variation on the modifications is not well known. Here, the authors find both shared and protein-specific genetic mechanisms regulating the glycosylation of two different proteins. PTMs alter the structure and properties of proteins and are thus involved in the dynamic regulation of most cellular events. It is common for a PTM enzyme to target multiple substrates or interact with multiple sites. For example, only 18 histone deacetylases target more than 3600 acetylation sites on 1750 proteins2. Environmental or pathological conditions can lead to dysregulation of PTM activities, which has been related to aging3 and several diseases, including cancer, diabetes, and neurodegeneration6. Despite their importance, little is known about the genetic regulation of posttranslational modifications.Posttranslational modifications (PTMs) are essential mechanisms used by cells to diversify and extend their protein functions beyond what is dictated by protein-coding sequences in the genome. These chemical reactions range from the addition of small moieties, such as phosphate (phosphorylation), complex biomolecules, as in glycosylation, to proteolytic cleavage7. Protein N-glycosylation is involved in a multitude of biological processes8. Accordingly, changes in N-glycosylation patterns have been associated with aging9 and a wide range of diseases, including Parkinson’s disease10, lower back pain11, rheumatoid arthritis12, ulcerative colitis13, Crohn’s disease13, type 2 diabetes14 and cancer17. However, for most of these conditions, it still remains to be clarified whether the disease causes changes in N-glycosylation or vice-versa. In addition, N-glycans are considered as potential therapeutic targets18 and prognostic biological markers21.N-glycosylation is one of the most common protein PTMs, where carbohydrate structures called glycans are covalently attached to an asparagine (Asn) residue of a polypeptide backbone. N-glycans are characterised by vast structural diversity and high complexity. While polypeptides are encoded by a single gene, N-glycan structures result from a sophisticated interplay of glycosyltransferases, glycosidases, transporters, transcription factors and other proteins29. IgG antibodies are one of the most abundant proteins in human serum, and their alternative N-glycosylation is suggested to trigger a different immune response and thus impacts the action of the immune system30. N-glycan structures are predominantly of the biantennary complex type and vary due to additions of core fucose, galactose, sialic acid and bisecting N-acetylglucosamine (GlcNAc), with disialylated digalactosylated biantennary glycan with core fucose and bisecting GlcNAc being the most complex N-glycan structure on IgG31. While a clear overlap in genetic control between total plasma proteins and IgG N-glycosylation was highlighted by previous studies24, it was not possible, until recently, to identify protein-specific N-glycosylation pathways for glycoproteins other than IgG due to technical challenges of their isolation in large cohorts.As with other PTMs, genetic regulation of N-glycosylation is not yet fully understood. Previous genome-wide association studies (GWAS) have so far focused either on the N-glycome of total blood plasma proteins as a whole or on glycosylation of one specific protein—immunoglobulin G (IgG)32. Human transferrin has two N-glycosylation sites—at the N432 and N630 residues, with biantennary disialylated digalactosylated glycan structure without fucose being the most abundant glycan attached34. We performed genome-wide association meta-analysis (GWAMA) of 35 transferrin N-glycan traits (N = 1890) and compared it with GWAMA of 24 IgG N-glycan traits (N = 2020) in European-descent cohorts, discovering both protein-specific and shared associations. For loci associated with the N-glycosylation PTM of both transferrin and IgG, we used colocalisation analysis to assess whether the underlying causal variants are protein-specific or rather shared between these proteins. We then suggested a molecular mechanism by which these independent causal variants could regulate the expression of glycosylation-related genes in different tissues.Here we investigate whether the same PTM of two proteins is regulated by the same genes and whether they are driven by the same causal genetic variants. We report genes associated with the regulation of transferrin N-glycosylation and compare these with the genetic regulation of glycosylation of a different protein (IgG). Transferrins are blood plasma glycoproteins regulating the level of iron in an organism. Iron plays a central role in many essential biochemical processes of human physiology: the cells’ need for iron in the face of potential danger as an oxidant has given rise to a complex system that tightly regulates iron levels, tissue distribution, and bioavailability26, so we focus here on glycosylation of transferrin. We performed GWAS of 35 ultra-high-performance liquid chromatography (UHPLC)-measured transferrin N-glycan traits and Haplotype Reference Consortium (HRC) r1.135—imputed genetic data in two cohorts of European descent, CROATIA-Korcula (N = 948) and VIKING (N = 952). Overall, we identified eight loci genome-wide significantly associated  with transferrin N-glycans in the CROATIA-Korcula cohort   and the transferrin (TF) gene. Cohort-specific heritability estimates for each transferrin glycan trait   with at least one of the 35 transferrin N-glycan traits , the fucosyltransferase locus (FUT8) and the sialyltransferase locus (ST3GAL4) . A more extensive GWAS on the genetic regulation of IgG glycosylation has already been publishedula N = 98 and VIKcis-protein quantitative trait locus (pQTL), rs817724039. This variant is associated with transferrin protein abundance and so can act as a proxy for protein levels and is not in linkage disequilibrium with glycan QTL (glyQTL) rs6785596, the sentinel glycosylation-associated SNP in TF (LD r2 = 0.02). Two glycans, TfGP3 and TfGP9, were significantly associated with transferrin cis-pQTL. However, both cis-pQTL and the glyQTL (rs6785596) contribute to the variation of TfGP3 levels, while only the cis-pQTL contributes to levels of TfGP9. Overall, this suggests that glycan associations with the TF gene are only completely accounted for by the transferrin protein levels in the case of TfGP9  encode glycosyltransferases, key enzymes in protein glycosylation, that have been previously associated with IgG and/or total plasma protein glycosylation  gene, which encodes the transferrin glycoprotein. A locus on chromosome 5 containing FOXI1 encodes a member of the forkhead family of transcription factors (Forkhead box I1). Finally, a locus on chromosome 8 contains the MSR1 gene, encoding the class A macrophage scavenger receptor, a trimeric integral membrane glycoprotein. Another gene of potential biological relevance at the chromosome 8 locus is the tumour suppressor candidate 3 (TUSC3), which encodes a protein localised to the endoplasmic reticulum and acts as a component of the oligosaccharyltransferase complex, responsible for N-linked protein glycosylation.The majority of genes that were closest to variants associated with transferrin N-glycosylation had a clear biological link to protein N-glycosylation. In particular, for five out of ten loci, the closest genes (i.e. r2 > 0.8) were identified to be significantly associated with the expression of multiple genes in several human tissues involved in transferrin metabolism  analysis followed by the Heterogeneity in Dependent Instruments (HEIDI) test40 to assess whether expression of these genes colocalises with transferrin glycosylation (TfGP) traits. SMR-HEIDI provided evidence of colocalisation, suggesting that the same underlying causal SNPs are likely to regulate both transferrin glycosylation traits and gene expression, for B3GAT1 in the liver and peripheral blood and ST3GAL4 in the liver  were classified as intronic, several SNPs were identified as missense variants: rs115399307 (chr5:169535155-T/C) causes the substitution of the non-polar, aliphatic amino acid isoleucine (I) to the polar, hydrophilic amino acid threonine (T) in the FOXI1 transcription factor. Similarly, NXPE4 variant rs550897  causes an amino acid substitution from tyrosine (Y) to histidine (H). Genetic variant rs41341748 (chr8:16012594-A/G) disrupts a stop codon sequence in MSR1, causing an elongated transcript with the amino acid arginine (Arg) added to the protein chain  maps to the enzyme’s catalytic domain and the allele T results in a change from negatively charged glutamic acid (E) to positively charged lysine (K), which leads to a full-length, but inactive, enzyme42. While the effect of reduced enzymatic activity on fucosylation of transferrin glycans needs to be experimentally validated, we observed that levels of TfGP32 are significantly lower in individuals carrying the T allele at rs17855739, compared to those with two C alleles  result in a change of amino acid sequence using the Ensembl Variant Effect Predictor (VEP) v9743 to assess if variants associated with transferrin N-glycosylation overlap transcription factor binding sites and hence may be hypothesised to affect transcription factor binding. From the list of prioritised genes, we selected the two encoding transcription factors, FOXI1 and HNF1A, and checked whether associated variants in the remaining eight loci were likely to affect binding of these transcription factors. Overall, the binding of both FOXI1 and HNF1A transcription factors might be affected by the sentinel variant  in the FUT8 gene. Similarly, the binding of HNF1A might be affected by the sentinel variants in the TF and ST3GAL4 loci 44, followed by SMR-HEIDI to determine whether the shared associations are caused by the same underlying causal variant . We observed an overlap of transferrin N-glycan-associated SNPs and their proxies with variants associated with a complex trait- and disease-associated variants for five out of ten glycosylation loci (Supplementary Data 11TF locus have been previously associated with serum concentration of carbohydrate-deficient transferrins (CDT) , there was no evidence of complex traits having an effect on glycan traits. However, we found positive associations of levels of TfGP14 and ulcerative colitis, and levels of TfGP28 and C-reactive protein levels, LDL and total cholesterol , following the same protocol. 13 loci were significantly associated with at least one of the 24 IgG N-glycan traits , but also with transcription factors , the protein itself  as well as other genes . Interestingly, the regions containing FUT8 and FUT6, genes encoding fucosyltransferases, enzymes adding core and antennary fucose, respectively, to the synthetised glycan, were associated with glycosylation of both proteins  and proceeded to test for colocalisation between glycosylation of the two proteins. We found a strong support against colocalisation in both genomic regions   and again, obtained robust evidence against the colocalisation hypothesis for all tested traits in both loci  Figs. . Since cFUT6 and FUT8 loci, we next explored the potential mechanisms behind these associations. The RSAT analysis suggests that the sentinel transferrin glycosylation SNP in the FUT8 region might be affecting the binding of the HNF1A transcription factor  is highly expressed in plasma cells and has low expression in hepatocytes, while the converse is true for TF , we identified ten significantly associated loci, two of which (near FOXI1 and MSR1) were never previously associated with the glycome of any protein. The other eight have been previously associated with glycosylation of transferrin, total plasma proteins and/or IgG . Here, we were able to measure 35 different transferrin glycan traits, providing higher resolution of underlying structures and insight into the overall transferrin N-glycome. The total plasma glycome quantifies the glycome of all proteins in plasma, but without information on which glycan was bound to which protein. Given that IgG and transferrin are among the most abundant plasma glycoproteins8, an overlap in genetic control of transferrin and IgG N-glycomes with that of total plasma proteins is to be expected. Sharapov et al.24. previously indicated that some of the genomic loci associated with the plasma glycome overlap with loci associated with IgG N-glycosylation. The present work suggests that the MGAT5, ST3GAL4 and B3GAT1 loci, that were also observed in the total plasma protein GWAS, might be capturing a signal within plasma protein glycosylation that comes from transferrin N-glycosylation.Posttranslational modifications (PTMs) are essential mechanisms that dynamically regulate a large portion of cellular events by altering the structure and properties of proteinsMGAT5, ST3GAL4 and B3GAT1 were only associated with transferrin while ST6GAL1 and MGAT3 were only associated with glycosylation of IgG. On the other hand, two fucosyltransferase genes, FUT8 and FUT6, were associated with both proteins. Since antennary fucose (produced by the FUT6 enzyme) is not typically found on IgG, we hypothesise that IgG glycosylation might be indirectly associated with FUT6 through antennary fucosylation of other enzymes or proteins involved in glycosylation of IgG.We then compared the genetic architecture underlying glycosylation of transferrin and IgG proteins. Using the GWAS from this study we showed that there are both protein-specific and shared genetic loci. Looking specifically at glycosyltransferase enzymes, the main ‘drivers’ of this posttranslational modification, that catalyse the transfer of saccharide moieties from a donor to an acceptor molecule, Even though the genes encoding FUT6 and FUT8 enzymes were associated with glycosylation of both proteins, using Approximate Bayes Factor colocalisation analysis, we showed that associations with transferrin and IgG N-glycosylation at these genomic regions are driven by independent underlying causal variants, where one variant regulates fucosylation of transferrin and the other of IgG. Our results suggest that while the same fucosyltransferase enzymes are involved in N-glycosylation of both transferrin and IgG proteins, the process is independently regulated by protein-specific causal variants.FUT8 locus is not in strong linkage disequilibrium (LD) with coding variants from the enzymes’ active site, suggesting that this is likely not a common mechanism of regulation of fucosylation of the two proteins. In addition, overall, SNPs associated with transferrin glycosylation predominantly mapped to regulatory rather than coding regions of the genome , this link, especially in the context of transferrin glycosylation, has yet to be experimentally proven. Overall, our data could suggest that the two different causal variants may affect the binding of different transcription factors in different tissues and therefore regulate the glycosylation of the two plasma proteins in a tissue-specific manner. However, the effect of specific SNPs on the binding of the two transcription factors and their downstream effect on the expression of fucosyltransferases in a tissue-specific manner still needs to be functionally validated.There are at least two mechanisms that could explain how different variants in an enzyme-coding gene could have distinct effects on two different substrates. If the two variants were in the coding region of the gene and affected the amino acid sequence of the enzyme, they could affect the enzyme’s specificity for binding each protein. However, the sentinel variant in the FUT8 locus associated with transferrin glycosylation might also be affecting the binding of the FOXI1 transcription factor. However, unlike HNF1A, a possible involvement of FOXI1 in the regulation of the transferrin fucosylation is to date unknown and would require functional validation. We also found that HNF1A binding could also be affected by variants associated with glycosylation in the TF and ST3GAL4 genes. While these relationships were hitherto undocumented and need further supporting evidence, they may suggest that HNF1A might regulate multiple genes associated with transferrin N-glycosylation.In addition to HNF1A, variants in the TF gene, rs6785596, can be considered an example of a ‘cis-glyQTL’: a genomic locus that explains variation in glycosylation levels and is local to the gene encoding the protein being glycosylated. Similar was observed for IgG glycosylation, where associated variants mapped to the IGH locus28, a genetic region encoding the heavy chain of immunoglobulin G. The transferrin glycosylation ‘cis-glyQTL’ is an eQTL for expression of transferrin in adipose tissue, but not in liver, where transferrin is predominantly expressed. The variant is also in middling LD (r2 = 0.57) with a missense variant, rs179989, providing a potential alternative explanation for the association. Altogether, the exact mechanism of how these ‘cis-glyQTL’ could be affecting glycosylation levels remains unclear. Considering causal relations between the transferrin glycome and complex traits and diseases, we found associations between levels of TfGP28 and C-reactive protein levels, LDL and total cholesterol. These associations were, however, driven by a single locus encoding the transcription factor HNF1A, suggesting that the locus might be pleiotropic and has an impact on both transferrin glycan levels and complex traits.The most strongly N-glycosylation-associated variant for the FUT8 and FUT6 genes. In the FUT8 region, these variants are likely to regulate fucosylation of transferrin and IgG in a tissue-specific manner, potentially acting through tissue-specific transcription factors. Additional studies, with larger sample sizes and focusing on other non-IgG proteins, will be necessary to further unravel the genetic architecture of N-glycosylation and to understand its relationship with human diseases and complex traits. While PTMs involved in intracellular signalling (e.g. phosphorylation) remain difficult to quantify in a high-throughput manner, here we investigated glycosylation of two plasma proteins, constraining the analysis to one type of PTM in the extracellular space. The impact of genetics on other, both intra- and extra-cellular posttranslational modifications will be an interesting area of future research.In conclusion, by performing the GWAS of the plasma transferrin N-glycome and comparing it with that of the IgG N-glycome, we were able to describe similarities and differences in the genetic regulation of posttranslational modification of two different proteins. When focusing on glycosyltransferases, the main enzymes of this PTM, we showed that there are associations specific to each protein, but also those that are involved in the glycosylation of both proteins. For the latter, we showed that fucosylation of transferrin and IgG are regulated by independent, protein-specific variants in the 50. Written informed consent was given and the study was approved by the Ethics Committee of the Medical School, University of Split . The Viking Health Study—Shetland (VIKING) is a family-based, cross-sectional study that seeks to identify genetic factors influencing cardiovascular and other disease risks in the population isolate of the Shetland Isles in northern Scotland51. Genetic diversity in this population is decreased compared to mainland Scotland, consistent with the high levels of endogamy. A total of, 2105 participants were recruited between 2013 and 2015, most having at least three grandparents from Shetland. Fasting blood samples were collected and many health-related phenotypes and environmental exposures were measured in each individual. All participants gave written informed consent and the study was approved by the South East Scotland Research Ethics Committee, NHS Lothian (reference: 12/SS/0151). Details of cohort-specific demographics, genotyping, quality control and imputation performed before GWAS can be found in Supplementary Data The CROATIA-Korcula isolated population cohort includes samples of blood DNA, plasma and serum, anthropometric and physical measurements, information related to general health, medical history, lifestyle and diet for ~3000 residents of the Croatian island of Korčula52 for transferrin and by Trbojević-Akmačić et al.53 for IgG. Briefly, proteins were first isolated from blood plasma (IgG depleted blood plasma in the case of transferrin) using affinity chromatography binding respectively to anti-transferrin antibodies plates for transferrin and protein G plates for IgG. The protein isolation step was followed by enzymatic release and labelling of N-glycans with 2-AB (2-aminobenzamide) fluorescent dye. IgG N-glycans have been released from total IgG . N-glycans were then separated and quantified by hydrophilic interaction ultra-high-performance liquid chromatography (HILIC-UHPLC). As a result, transferrin and total IgG samples were separated into 35 (transferrin: TfGP1 − TfGP35) and 24 (IgG: GP1 − GP24) chromatographic peaks. It is worth noting that there is no correspondence structure-wise between transferrin TfGP and IgG GP traits labelled with the same number.Transferrin and total IgG N-glycome quantification for CROATIA-Korcula and VIKING samples was performed at Genos Glycobiology Laboratory. Isolation of the protein of interest and N-glycan quantification is described in more detail in Supplementary Materials and Methods and in Trbojević-Akmačić et al.10-transformed. Batch correction was then performed using the empirical Bayes approach implemented in the ‘ComBat’ function of the sva 3.34.0 R package54, modelling the technical source of variation (96-well plate number) as a batch covariate. Batch corrected measurements were then exponentiated back to the original scale.Prior to genetic analysis, raw N-glycan UHPLC data was normalised and batch corrected to reduce the experimental variation in measurements. Total area normalisation was performed by dividing the area of each chromatographic peak (35 for transferrin and 24 for IgG) by the total area of the corresponding chromatogram. Resulting measures are therefore relative abundances of each glycan structure in the overall glycosylation profile. Due to the multiplicative nature of measurement error and right-skewness of glycan data, normalised glycan measurements were log55 and then adjusted for age and sex, as fixed effects, and relatedness  as a random effect in a linear mixed model, calculated using the ‘polygenic’ function from the GenABEL R package55. Residuals of covariate and relatedness correction were tested for association with HRC (Haplotype Reference Consortium)35 imputed SNP dosages using the RegScan v0.5 software56, applying an additive genetic model of association.Genome-wide association analyses (GWAS) were performed in the two cohorts of European descent, CROATIA-Korcula and VIKING. Associations with 35 transferrin N-glycan traits were performed in 948 samples from CROATIA-Korcula and 959 samples from VIKING. Associations with 24 IgG N-glycan traits were performed in 951 samples from CROATIA-Korcula and 1086 samples from VIKING. The sample size of the same cohort differs between transferrin and IgG due to the different number of samples successfully measured for each protein. Prior to GWAS, each glycan trait was rank transformed to normal distribution using the ‘rntransform’ function from the GenABEL 1.1-6 R packageN = 1890 for transferrin and N = 2020 for IgG N-glycans, for ~10.7 million SNPs) using METAL v2011-03-25 software57, applying the fixed-effect inverse-variance method, followed by genomic control correction. The mean genomic control inflation factor (λGC) was 0.997 (range 0.982–1.011) for transferrin N-glycans and 0.995 (range 0.981–1.008) for IgG N-glycans meta-analysis, showing that the confounding effects of the family structure were correctly accounted for.Prior to meta-analysis, the following quality control was performed on cohort-level GWAS summary statistics. We removed all SNPs with a difference in allele frequency between the two cohorts higher than ±0.3 , as well as variants showing a minor allele count (MAC) lower or equal to 6 . Cohort-level GWAS were then meta-analysed  from the sentinel SNP in the same locus. To obtain a unique list of loci that are independent of the specific glycan trait, we then merged this list of sentinel SNP-glycan trait pairs for all 35 glycan traits and applied a similar procedure - all SNP-glycan trait pairs within a 1000 kb window (±500 kb from sentinel SNP) were grouped in the same locus, resulting in a unique list of sentinel SNP-top glycan trait pairs, summarising the genomic regions most strongly associated with N-glycans across all traits. A visual representation of the procedure can be seen in Supplementary Fig. 58 and visually checked—in case of overlapping patterns of association, only the sentinel SNP-top glycan trait pair showed the lowest p value was selected as a locus representative.We used a positional approach to define genomic regions (loci) significantly associated with transferrin N-glycan traits, following the procedure adopted by Sharapov et al.TF gene are driven by protein levels, we tested the association of transferrin cis-pQTL rs817724039 with transferrin glycosylation using the likelihood ratio test implemented in the lmtest 0.9-38 R package59 between the following models:TF gene region.To assess the potential impact of transferrin protein levels on transferrin glycome associations and to check whether the associations in the region of the The likelihood ratio tests were performed between:M0 and M1 to assess associations of glycans and pQTL (rs8177240)M1 and M3 to assess whether glyQTL contributes to glycan levels even when the pQTL is included in the modelM2 and M3 to assess whether pQTL contributes to glycan levels even when the glyQTL is included in the model60, with age, sex, pQTL and glyQTL as fixed effects and kinship matrix as a random effect. The kinship matrix was estimated from the genotyped data using the ‘ibs’ function from GenABEL55 R package.To control for increased levels of relatedness between subjects in our studies, the models were fitted using linear mixed models as implemented in the lme4qtl 0.0.2 R package26 and is beyond the scope of the present work.The meta-analysis follow-up analyses were performed only for the transferrin N-glycans meta-analysis, since genetic regulation of IgG N-glycosylation has already been explored in a larger, IgG-specific study38 1.91.4beta stepwise model selection, ‘cojo-slct’, with the IgG and transferrin N-glycan meta-analysis summary statistics and genotypes of 10,000 unrelated individuals of white British ancestry from UK Biobank61 as independent LD reference panel. Collinearity was restricted to 0.9 and the p value threshold was set to 1.43 × 10−9 for transferrin and to 2.08 × 10−9 for IgG. Reported joint p values were then adjusted by the genomic control method62. The list of samples for the independent LD reference panel was created with R 3.6.0, while the panel itself was generated using Plink 2.063. After sample extraction from the UK Biobank full dataset, SNP deduplication was performed both by position  and marker name . We acknowledge that UK Biobank might not be a perfect reference population for the CROATIA-Korcula cohort, however, there are no other reference panels with suitable ancestry and sample size (>4000)38. The proportion of variance (var) in phenotype (Y) explained by independently associated SNPs at each transferrin N-glycans associated locus was calculated with the following formulaβ is the effect estimate for the SNP and phenotype association at the locus, Y covariates adjusted residuals are the residuals resulting from the adjustment of the phenotype by age and sex, as fixed effects and relatedness  as a random effect in a linear mixed model. The ‘polygenic’ function from the GenABEL R package was used also to estimate cohort-specific heritability for each transferrin glycan trait.To capture the overall contribution to phenotypic variation at each genomic region and identify secondary association signals at a locus, we performed approximate conditional analysis using the GCTA-COJO64. Genes having a clear biological link to protein N-glycosylation  and genes previously associated with IgG and/or total blood plasma proteins N-glycome were given a priority. The overlap of independent significant SNPs identified by COJO with eQTL was investigated using PhenoScanner v1.1 database44, taking into account significant genetic association  at the same or strongly (LD r2 > 0.8) linked SNPs in populations of European ancestry. The Ensembl Variant Effect Predictor (VEP v97) tool41 was used to determine putative functional effect and impact on a transcript or protein of independent significant SNPs and their strongly (LD r2 > 0.8) linked SNPs in populations of European ancestry. Among genes prioritised so far, two were transcription factors (i.e. HNF1A and FOXI1), while the remaining were non-transcription factor protein-coding genes . Using the Regulatory sequence analysis tools (RSAT) v2018-08-04 programme matrix-scan43, we applied a pattern-matching procedure to search for sequences recognised as binding sites for HNF1A and FOXI1 transcription factors in associated regions of the other eight prioritised genes. Position-specific scoring matrices (PSSMs), representing the frequency of each nucleotide at each position of the transcription factor motif, were downloaded for HNF1A and FOXI1 from the JASPAR65 database. For each of the eight genomic regions explored for possible transcription factor binding sites, we included the most strongly associated SNP and a 60 bp surrounding sequence (30 bp on either side of the sentinel SNP). The significance threshold was set to the p value ≤ 0.003, Bonferroni corrected for 16 tests performed (eight putative transcription factor binding sites tested for two transcription factors).For all genome-wide significant loci, we suggested plausible candidate genes combining different evidence, namely evaluating biological role in the context of protein N-glycosylation of genes nearest to sentinel variants , assessing colocalisation with gene expression  or investigating associated variant’s predicted effects on the protein sequence or on putative transcription factor binding sites. Positional gene mapping was performed using FUMA v1.3.5e SNP2GENE function44 was used to investigate the overlap of significant transferrin glycosylation SNPs with gene expression levels and complex human traits. As previously described, we considered traits with genome-wide significant association  at the same or strongly (LD r2 > 0.8) linked SNPs in populations of European ancestry. We then used Summary data-based Mendelian Randomisation (SMR) analysis followed by the Heterogeneity in Dependent Instruments (HEIDI) test40 to assess whether overlapping expression and complex traits, identified by PhenoScanner, were also colocalising with transferrin glycosylation (TfGP) traits. The SMR test indicates whether two traits are associated with the same locus, and the HEIDI test specifies whether both traits are affected by the same underlying functional SNP. Each of ten sentinel SNPs–TfGP pair (Table 66 (http://cnsgenomics.com/software/smr/#eQTLsummarydata), the CEDAR project67 (http://cedar-web.giga.ulg.ac.be/) and the GTEx project version 768 . Summary statistics for complex traits were obtained from various resources. In total, we used data for three tissues/cell types: CD19 + B-lymphocytes (CEDAR), GTEx liver (GTEx) and peripheral blood (the Blood eQTL study) and eight complex traits. A full list of GWAS collections, tissues and complex traits see in Supplementary Data 40. We used sets of SNPs having the following properties: (1) being located within ±250 kb from the sentinel SNPs identified in the present study; (2) being present in both the primary GWAS and eQTL data/GWAS for the complex trait; (3) having MAF ≥ 0.03 in both datasets; (4) having squared Z-test value ≥10 in the primary GWAS. Those SNPs that met criteria (1), (2), (3), (4), had the lowest p value in the primary GWAS and were in high LD (r2 > 0.8) with the sentinel SNPs were used as instrumental variables to elucidate the relationship between gene expression/disease and TfGP (we define them as ‘top SNPs’). It should be noted that SMR/HEIDI analysis does not identify a causative SNP affecting both traits. It can be either the top SNP or any other SNP in strong LD. After defining the set of eligible SNPs for each locus, we made the ‘target’ and ‘rejected’ SNP sets and added the top SNP to the ‘target’ set. Then we performed the following iterative procedure of SNP filtration: if the SNP from the eligible SNP set with the lowest PSMR had r2 > 0.9 with any SNP in the ‘target’ SNP set, it was added to the ‘rejected’ set; otherwise, it was added to the ‘target’ set. The procedure was repeated until the eligible SNP set was exhausted, or the ‘target’ set had 20 SNPs. If we were unable to select three or more SNPs, the HEIDI test was not conducted. HEIDI statistics was calculated asThe PhenoScanner v1.1 database−4 . Inference of whether a functional variant may be shared between the TfGP and gene expression/disease were made based on the HEIDI test: PHEIDI ≥ 0.001 (possibly shared) and PHEIDI < 0.001 (sharing is unlikely).The results of the SMR test were considered statistically significant if PSMR < 1.7 × 1069. MR uses genetic variants as instrumental variables to investigate the effects of one trait (exposure) on another trait (outcome), assuming that the instrumental variables associate with the outcome only through exposure. GWAS summary statistics for complex traits were obtained from the IEU GWAS database70 and their references are listed in Supplementary Data p value < 5 × 10−8) and independent . To distinguish causal relationships from confounding by LD, we followed up significant MR tests  with approximate Bayes factor colocalisation analysis, developed by Giambartolomei et al.71 and implemented in the ‘coloc.abf’ function from the coloc 4.0-6 R package, using default priors of 10−4 for the prior probability of SNP being associated with trait 1 or trait 2 (p1 and p2) and 10−5 for the prior probability of an SNP being associated with both traits (p12). To further assess the robustness of our findings, where available, we performed the ‘coloc’ analysis using a different complex-trait GWAS dataset compared to the SMR-HEIDI analysis , as implemented in the TwoSampleMR 0.5.6 R packageFUT8 and FUT6 genomic regions were significantly associated with both transferrin and IgG N-glycans. To investigate a possible overlap in genetic control of glycosylation between the two proteins, we used the approximate Bayes factor colocalisation analysis implemented in coloc R package71, followed by pairwise conditional and colocalization analysis (PwCoCo)46 in case of multiple independent variants contributing to the trait variation. A posterior probability >80% was considered as robust evidence supporting the tested hypothesis.The p value −8) are regulated by the same underlying variants. For each protein (i.e. transferrin and IgG) and each genomic region (i.e. FUT8 and FUT6), we tested separately the group of glycans carrying only one independent association signal at the locus and the group of glycan traits showing multiple independent signals of association , for a total of nine pairwise combinations. Secondary association signals at FUT8 and FUT6 loci for both transferrin and IgG N-glycans were assessed using GCTA-COJO approximate conditional analysis stepwise model selection38 and an LD reference panel of 10,000 unrelated, white British ancestry individuals from UK Biobank61. We then performed the association analysis conditional on identified secondary association signals at FUT8 and FUT6 loci using GCTA-COJO38 ‘cojo-cond’ and the same 10,000 UK Biobank samples LD reference panel, with 5 × 10−8p value threshold and used those for pairwise colocalisation analyses.For glycan traits with multiple independent association signals and lacking strong evidence for colocalisation, we applied the PwCoCoTF, IGHG1, HNF1A and IKZF1, expressed in gene counts, for hepatocytes (529 samples) and plasma cells (648 samples) was obtained from ARCHS4 portal72. Samples with a total number of gene count less than 5,000,000 were filtered out, leaving 513 hepatocyte and 53 plasma cell samples for the analysis. Gene counts were scaled to transcripts per million (TPM) and log2(1 + TPM) transformed.Gene expression data for Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-19Reporting Summary


In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term ‘variance stratification’. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI. Pooling participant-level genetic data into a single analysis can result in variance stratification, reducing statistical performance. Here, the authors develop variant-specific inflation factors to assess variance stratification and apply this to pooled individual-level data from whole genome sequencing. Unless appropriately accounted for in the analysis, e.g., by using regression-based adjustments for ancestry such as principal components or genetic relatedness matrices in linear mixed models, or their combination, it can lead to false–positive associations5. Population stratification more generally can refer to differences in phenotype distribution and allele frequency across population subgroups6, and hence can also manifest as differences in phenotype variances by subgroup, combined with differences in allele frequencies. In practice, this phenomenon is common in pooled analysis of multi-study data, as small differences in allele frequencies are prevalent, and different studies being pooled often have different measurement protocols, environmental exposures and inclusion criteria, all of which can lead to different phenotype variances among studies.Large-scale association analyses using whole genome sequence (WGS) data on thousands of participants are now underway, through programs such as the NHLBI’s Trans-Omics for precision Medicine (TOPMed) program and NHGRI’s Genome Sequencing Program. Unlike earlier Genome-Wide Association Studies (GWASs), where data were combined using meta-analyses of summary statistics, in WGS analyses participant-level data from multiple studies are often pooled, and results are obtained from a single analysis. Pooled analysis of WGS is useful due to its computational tractability and its ability to control for genetic relatedness across the pooled datasets. However, it is sensitive to a form of population stratification that is not well known. Population stratification in genetic association analysis6 studied the effect of population stratification due to mean differences, variance differences, and more generally, phenotypic heterogeneity, across subpopulations, on false positive detections when testing variant associations with a quantitative trait. Conomos et al.7 showed that when testing variant associations in a pooled sample of Hispanics/Latinos from different Hispanic background groups, statistical properties of tests are improved when the model allows for different residual variances in the different background groups. Musharoff et al.8, in a preprint, studied population variance structure using statistical models of both population means and variances, and developed statistical tests for the association of genetic variants with phenotypic variability.Previous studies considered the effect of combining together groups with different phenotypic variances. Haldar and Ghoshλvs, which quantify the degree of inflation/deflation in association testing of a single genetic variant due to population stratification at the variance level. We develop an algorithm to compute approximate variant-specific inflation factors based on allele frequencies and variances in groups pooled together for analysis, demonstrate their usage for assessing model fit, and demonstrate the implications of the population stratification at the variance level in simulations and in analyses of WGS data from TOPMed. To account for population stratification at the variance level we use the computationally efficient and scalable approach proposed in Conomos et al.7 and implemented in GENESIS9, and show in simulations that it indeed addresses the variance stratification problem in scenarios based on Musharoff et al.8.In this manuscript, we develop variant-specific inflation factors λvs: empirical variances based on homogeneous and stratified variance models, and model-based variances from the heterogeneous variance model. The estimated λvs were essentially the same regardless of the method. Figure λvs to the observed λgc in each of the simulation settings and in each of the two modeling approaches (homogeneous versus stratified variance). Settings are divided according to patterns determining whether variance stratification will be expected, including same or different MAFs between the two studies, the same or different error variances, and whether the PC affects the genetic variance or not. The top three rows in Fig. λvs and the observed λgc are highly correlated. In contrast, the stratified variance model was robust to variance stratification across all settings, with observed inflation around 1 in all simulations. The bottom two rows of Fig. λvs is always 1 (no inflation). As expected, the observed inflation is the same in the homogeneous and stratified variance models. The spread seen in the values of the observed inflation, with some values higher and some lower than the desired 1, are consistent with that expected based on the number of replication of simulations in each setting ; see Our simulations consisted of 576 simulation settings according to various combinations of parameters. We compared a few ways to estimate variance parameters to be used in computing N = 7596; three analysis groups) and body mass index  in the TOPMed freeze 4 dataset. In both analyses we computed approximate variant-specific inflation factors λvs. We investigated the inflation/deflation problems resulting from variance stratification, and verified that the patterns of inflation and deflation in the homogeneous variance analysis agree, across the different variants, with those obtained from the formula and the provided code. Figures λvs “Approx. no inflation”  for variants where allele frequency is greater in this subgroup compared to other subgroups, or inflation  for variants with lower allele frequency in this subgroup compared to others.9). Our mathematical derivation and code can be used to assess the degree of miscalibration of association tests. The code uses an additive model, using a Binomial distribution for allele counts, which is commonly used in GWAS. Inflation/deflation trends should be similar between additive and dominant models, though specific values estimated using each of the two models would not be identical.While default linear regression methods assume the same variance for all subgroups, which leads to mis-calibrated tests if the assumption does not hold, standard computational tools can be adapted to allow for a stratified variance model, yielding better calibrated tests. Specifically, by fitting different residual variances for each study, or more generally, appropriately defined “analysis group”  the problem can be alleviated. This can be viewed as fitting a different variance component for noise within each study, or as a weighted least squares approach, in which the group-dependent weights are estimated. This approach is implemented in some standard genetic analysis software packages  the variance stratification model clearly suffices to account for variance heterogeneity, in the mixed model setting, a residual variance stratification model may not be optimal, because it may not fully account for stratification in the genetic variance, which could be the result of study design. For example, in Fig. 15. MetaCor was shown to have good statistical properties and performed well in the BMI and HGB analysis. However, it is currently computationally costlier than a pooled analysis because individual level data are used both at the individual analysis group computations, and when computing covariances between effect size estimates of all analysis groups. Computational efficiency is critical when testing the large number of variants observed in WGS studies. In addition, the MetaCor approach is not yet extended to tests of sets of rare variants (rather than single rare variants tests studied in the current manuscript). While more difficult to assess, variance stratification likely affects tests of rare variant sets as well, and methods that use a Score test based on a null model that is fit once, such as the stratified variance approach implemented in GENESIS, straightforwardly extend to such settings. As sample sizes of TOPMed grow, pooling together more diverse studies and populations, variance stratification problems may be more severe. Models allowing for pooled analysis with both group-specific residual and genetic variances or robust variance estimates may be needed for better control of Type I errors and increased efficiency. Until such methods are developed, we recommend to first use the stratified variance approach, because it is computationally efficient, it can account for relatedness across the entire sample, and the same null model can be used to test variant sets. As a second step, we recommend computing approximate λvs, and assessing whether observed inflation/deflation remains for test statistics within groups of variants predicted to be inflated/deflated based on λvs values. If inflation/deflation are observed despite residual variance stratification, the analyst would ideally move forward with a meta-analytic approach such as MetaCor , or standard meta-analysis after removing individuals to generate genetically independent strata.In linear regression, the stratified residual variance model allows every analysis group to have its own residual variance parameter. In the mixed model setting, where the variance is decomposed into genetic and residual variances, this model keeps the genetic variance component the same but allows for the residual variance to differ across groups. Analysis groups can be defined as study, race/ethnicity, combinations of these, or any other sample characteristics that affect trait variance and may also correlate with allele frequencies. Our mathematical derivation and code for computing n, we assume that the data follow a linear model denoted asyi is the trait or phenotype value of person i, gi is their count of coded alleles , β0 denotes the mean outcome in those with no copies of the coded allele, β denotes the effect on the mean trait of each additional copy of the coded allele, and the For a total sample size of β = 0), and that the errors follow a normal distribution p is the frequency of having any copies of the variant allele, and gi is used in analysis.To provide intuition for the variance stratification problem, we first demonstrate a mathematical derivation in simplified settings. We assume that the genotype effect is null  isThe Wald test quantifies the strength of the genetic association by dividing a regression-based estimate of i by σ2 is the common variance parameter. To illustrate how this approach can mislead under variance stratification, we consider the situation where two studies are present, of sizes n1 and n2, respectively, such that p1 and p2 for the frequency of the variant of interest (under dominant mode). Because we assume that the variant was centered in the pooled population, we have that p1 = p2, the two forms4 and5 are equal, as there is no association between genotype and outcome, and no confounding occurs. But when M studies.Denoting the estimated residual variance of individual 8 introduced a model where the variances depends on individual-specific genetic components. For example, it could depend on a principal component (PC) of the genetic data, with: θg is a PC, with values varying across individuals. We address this setting in simulations.In some studies, researchers use mixed models in GWAS to account for genetic relationships between individuals. Then, it is usually assumed that the variance is decomposed to an error and genetic variances, so that 10, or for more detail Sections 5.2–5.3 of Van der Vaart11. We now allow for additive genetic model, and do not assume that the genotypes are standardized. The variance estimator used by the Wald test is now provided as follows:We can use mathematical derivations under homogeneity and heterogeneity, relaxing the restrictive assumptions provided earlier, to compute variant-specific inflation factors. These make use of standard “sandwich” formula for large-sample approximations of the variance of estimators; for a minimally-technical summary see Result 2.1 in Wakefieldgi = 1 and are from the same study, having the same residual variance). The algorithm below uses the additional assumption that phenotypic variance within each study does not vary with genotype—which must hold under the strong null hypothesis of no association in any subpopulation. It also uses the simplifying assumption that variants are in Hardy Weinberg Equilibrium (HWE) within each study population; testing HWE is a standard preprocessing step for genotype data.Based on these two expressions, we propose an algorithm to compute an approximate variant-specific inflation factor. For computational purposes, we further simplify these arguments taking advantage of the fact that there are repeated rows  to similarly replace WTVW by its large sample limit under HWE. Specifically, define:M of the form M × M design matrix modeling study-specific intercepts where the i, j element Dij isM × 3M diagonal matrix, in which each entry gives the population proportion in each combination of genotype and study, i.e.,:P, a 3Notice that the quantity M × 3M diagonal matrix, in which each entry gives the outcome variance in each combination of genotype and study.V, is a 3λgc, the genomic control inflation factor12 that would be obtained by comparing the median Wald test statistic to the median of the λvs, for “variant specific”. Note that this function requires estimation of variances  and allele frequencies (for constructing matrix V), which are readily obtained.Define now https://github.com/tamartsi/Variant_specific_inflation, and the function will be integrated into the GENESIS R package.An R function implementing these matrix calculations is provided, together with a tutorial that includes a coding example. These are also provided on GitHub on λvs, in terms of how it approximates the standard genomic control coefficient λgc obtained from a “homogeneous variance” model that estimates a single variance parameter across data from all studies. We also studied whether a “stratified variance” model, allowing for different variance parameters across two studies, improves upon the homogeneous variances model. In this vein, we simulated unassociated genetic variants and outcomes in a range of settings combining two studies. We simulated yi be the outcome value of person μ2 in study 2 computed such that the overall PC mean in the two studies together is equal to zero . We varied the following parameters: Inλgc as the ratio between median observed and expected value of the λvs in each of the 10,000 simulations based estimated variances and observed allele frequencies in each of the two simulated studies, and averaged these estimates across the simulations. We compared three approaches to estimate variances1: fit a homogeneous variances model, obtain residuals n is the number of study individuals 2; fit a “stratified variance” linear regression model allowing for different residual variances by study (as implemented in the R/Bioconductor package GENESIS9);3 use the same model with stratified variances, but use the variance estimates obtained by the AI-REML algorithm (model variance). In the Supplementary Information, we provide a distribution of λgc values that would be seen under random variability, using 10,000 independent test statistics (as was used in simulations), and simulating test statistics under the null and computing inflation factors λgc.We performed 10,000 simulations for each combination of parameters and, for each such setting, computed https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/document.cgi?study_id=phs000964.v2.p1&phv=251960&phd=6969&pha=&pht=4838&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1).For the present analysis, we used Whole Genome Sequencing (WGS) data from freeze 4 of TOPMed. WGS was performed on DNA samples extracted from blood. Sequencing was performed by the Broad Institute of MIT and Harvard (FHS and Amish) and by the Northwest Genome Center (JHS). PCR-free libraries were constructed using commercially available kits from KAPA Biosystems (Broad) or Illumina TruSeq (NWGC). Libraries were pooled for clustering and sequencing, and later de-multiplexed using barcodes. Cluster amplification and sequencing were performed according to manufacturer’s protocols using the Illumina cBot and HiSeq X sequencer, to a read depth of >30X. Base calling was performed using Illumina’s Real Time Analysis 2 (RTA2) software. Read alignment, variant detection, genotype calling and variant filtering were performed by the TOPMed Informatics Research Center (University of Michigan). Reads were aligned to the 1000 Genomes hs37d5 decoy reference sequence. Variant detection and genotype calling were performed jointly for several TOPMed studies , using the GotCloud pipeline. Mendelian consistency was used to train a variant quality classifier using a Support Vector Machine, used for variant filtering. Additional quality control , performed by the TOPMed Data Coordinating Center, were used to detect and resolve sample identity issues. Further details (including software versions) are provided online  on all available variants for the corresponding trait with minor allele frequency at least 0.001, which was used to control for genetic relatedness in mixed models. Because some studies had individuals with different genetic backgrounds , we defined “analysis groups” to use for assessment of variance stratification. An analysis group was as either all individuals from a single study , or further defined by both study and race/ethnic group . Thus, analysis groups capture multiple potential sources of trait variance, including differences in allele frequencies due to genetic ancestry, differences in environment and social/cultural factors, and differences in trait measurement by study. For both BMI and HGB, we performed single-variant association analysis for all variants with minor allele count of at least 20. Detailed breakdown of the studies and populations used in these analyses are provided in the Tables 14. Thus, we first fit a mixed linear regression model, with fixed effects for sex, age , group defined by study and race/ethnicity, and allowing for genetic relatedness by including a variance component proportional to the GRM. Then we took the residuals generated by this model, rank-normalized them, and then re-fit the same model but with the rank-normalized residuals as the trait. For both traits, we compared four analyses: first, a ‘homogeneous variance’ analysis that estimates a single residual variance parameter across all individuals; second, a “stratified residual variance” model that allows a different residual variance parameter for each analysis group; third, a “completely stratified” approach which fits models and performs tests in each analysis group separately, and then combines the results via inverse-variance fixed-effects meta-analysis; and forth, a “MetaCor” analysis15 that perform stratified analyses followed by fixed-effects meta-analysis while accounting for potential correlations due to genetic relationships between individuals in different analysis groups. The ‘completely stratified’ and the ‘MetaCor’ analyses are slightly more flexible than the stratified variance model because they allow for different genetic variance components across analysis groups, in addition to different residual variance components. For BMI, we removed eight individuals from the “completely stratified” analysis to ensure individuals were unrelated across groups, defined as less than third-degree relatedness. All analyses, other than MetaCor, used the GENESIS R package.To demonstrate the variance stratification problem, we used datasets of hemoglobin concentrations (HGB) and body mass index (BMI) in the TOPMed freeze 4. For each of the traits, we computed a Genetic Relationship Matrix GRM1 on all aλvs and, for diagnostics, generated QQ-plots as describe later. Notably, λvs were developed assuming independent data, and applying them in the mixed model settings provides only an approximation, as both the sample size is inaccurate , and there is more than a single variance parameter, and thus it is not straight forward to decide which variance estimates to use in computing λvs. To see that, consider the mixed- model analysis. We modeled both an error and a genetic variance component, so that for each observation, the model, in matrix form, assumes that:G is the GRM, and GWe studied the calibration of the various analyses of HGB and BMI by computing approximate variant-specific inflation factors λvs computations, due to changes to the residual distributions due to rank-normalization. Because we are alerting the readers to the problems arising from assuming that variances are the same across all studies, we used variance computed based on the ‘homogeneous variance’ null model . We extracted marginal residuals  for each group, and computed empirical variance for group j by In addition, we applied the fully-adjusted two-stage procedure for rank-normalization of residuals, another procedure unaccounted for by the algorithm. Therefore, different possible models will yield quite different variance estimates to be used in the λvs are computed for each of the variants of interest, we propose to generate QQ-plots across sets of variants to visualize whether population stratification at the variance level is appropriately addressed. A function is available on the GitHub repository to generate QQ-plots stratifying variants to categories: “Inflated”, “Deflated” and “Approx. no inflation”. The categories can be manually defined, so that a variant can be assigned to the “Inflated” category if its λvs is larger than a user-specified value, e.g., 1.01. Similarly, a variant is assigned to the “Deflated” category if its λvs is lower than a user-specified, e.g., 0.99. Variants are assigned to the “Approx. no inflation” category if they are not in the “Inflated” or “Deflated” categories, i.e., their λvs is close to the desired value of 1.Once λvs for each allele frequency category: <0.01, 0.01–0.05, 0.05–0.2, and 0.2–0.5. We also studied the similarity of inflation/deflation patterns between BMI and HGB.To study how common the variant inflation/deflation problem is, and how it relates to variant frequencies, we computed the proportion of variants in levels of Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Software 1Reporting Summary