EDNRB and CDK10 as significantly associated with hair colour. Our results provide insights on the mechanisms regulating pigmentation biology in humans.Hair colour is a polygenic phenotype that results from differences in the amount and ratio of melanins located in the hair bulb. Genome-wide association studies (GWAS) have identified many loci involved in the pigmentation pathway affecting hair colour. However, most of the associated loci overlap non-protein coding regions and many of the molecular mechanisms underlying pigmentation variation are still not understood. Here, we conduct GWAS meta-analyses of hair colour in a Canadian cohort of 12,741 individuals of European ancestry. By performing fine-mapping analyses we identify candidate causal variants in pigmentation loci associated with blonde, red and brown hair colour. Additionally, we observe colocalization of several GWAS hits with expression and methylation quantitative trait loci (QTLs) of cultured melanocytes. Finally, transcriptome-wide association studies (TWAS) further nominate the expression of  Frida Lona-Durazo et al. perform a GWAS of human hair colour in a Canadian population cohort collected across multiple provinces. By combining these results with transcriptomic and methylation data, the authors provide further insight into the underlying biology of hair colour. Contrary to other complex traits, the environment has little or no effect on pigmentary traits, with the exception of facultative skin pigmentation (i.e. tanning ability)7. Therefore, the wide range of diversity in pigmentary traits across worldwide populations is mainly due to genetic variation that has been shaped by a range of evolutionary factors 12. Elucidating the genetic architecture of pigmentary traits may aid in the general understanding of biological pathways, gene interactions, their regulation and expression. Likewise, it has the potential to further identify molecular mechanisms associated to important diseases, such as skin cancer .Pigmentary traits in humans  have a polygenic architecture, in which pleiotropy and epistasis are common phenomenaOCA2, TYR, SLC24A5) have been associated with multiple pigmentary phenotypes, whereas other genes have been associated with only one of these traits 13. Adding to this complexity, previous studies have identified the presence of allelic heterogeneity, in which different variants within a single gene are associated with pigmentation variation in different populations 14, as well as the effect of multiple independent variants on the same locus associated with a diverse range of pigmentation tones within populations 19. Furthermore, some genes associated with pigmentary phenotypes  are also known to increase the risk of cutaneous melanoma26. Thus, the link between pigmentation and cancer risk also highlights the biomedical importance of efforts to characterise the genetic architecture of pigmentary phenotypes27.Current evidence indicates that there is a partial overlap in the genetic architecture of hair, eye and skin pigmentation. Some genes  have uncovered numerous loci associated with hair colour in people of European ancestry by using discrete hair colour categories as an approximation33. By analyzing the UK Biobank (UKBB) data, both of these studies identified hundreds of regions associated with the phenotype in question across the genome, some of which had not been previously identified, due to the lack of power to detect significant associations .Hair colour is a quantitative phenotype that results from differences in the amount and ratio of eumelanin and pheomelanin synthesised in melanocytes located in the hair bulb, which then migrate to the hair shaft33. In spite of these advances, for most of the hair pigmentation-associated loci, the causal variants and the molecular mechanisms underlying pigmentation variation remain to be identified34. This is in fact a challenging task, given that most of the top SNPs associated with hair colour are located in nonprotein-coding regions of the genome, with no obvious or direct function on the trait, thus hinting to a regulatory function.Furthermore, Morgan and colleagues provided a detailed analysis of the loci associated with red hair colour, including penetrance and interactions among single-nucleotide polymorphisms (SNPs), offering insights on the genetic complexity of this hair colour tone35. For instance, statistical fine-mapping methods have become computationally feasible and make it possible to model multiple causal variants simultaneously36. Additionally, when putative causal variants are identified, they can be further explored by evaluating their effect on the regulatory profile of target genes on relevant cell types, which can be statistically tested with colocalization or transcriptome-wide association studies (TWAS) approaches38.Major advances in the characterisation of regulatory elements, along with the development of a diverse set of computational tools using GWAS summary statistics, have facilitated the interpretation of GWAS hits of several polygenic phenotypesIn this study, we conducted a meta-analysis of genome-wide association studies including 12,741 Canadian participants of European-related ancestry from the Canadian Partnership for Tomorrow’s Health. We focused our efforts on identifying candidate causal variants and target genes in complex genomic regions known to alter hair colour, by performing a wide range of post-GWAS analyses. Our main outcomes include the identification of multiple candidates causal variants through fine-mapping of significant loci across distinct regions of the genome, including putative causal variants not previously reported for hair colour, and the colocalization of GWAS loci with gene expression and methylation quantitative trait loci  using cultured human primary melanocytes. Finally, we conducted transcriptome-wide association studies (TWAS) of hair colour with cultured melanocyte expression data.N = 824), dark brown , light brown , blonde , red (N = 306) hair colour, NA (N = 30). After quality control of the genotypes  we kept 12,741 individuals for further analyses.12,996 participants of the Canadian Partnership for Tomorrow’s Health (CanPath) project, who were genotyped using different genome-wide genotyping arrays (See Methods for details), self-reported their natural hair colour (before greying) using six possible answers: black  blonde vs. brown (light and dark) + black hair colour; 2) brown (light and dark) vs. black hair colour; and 3) red vs. brown (light and dark) + black hair colour. For the third model (red vs. brown + black hair colour), the number of individuals with red hair colour on two of the genotyping arrays (GSA 24v1 and Omni 2.5) was less than 20, therefore we excluded these two arrays from the analysis  using a logistic mixed model on SAIGE (version 0.38)42. In total, the number of individuals included on each meta-analysis was: blonde vs. brown + black hair colour: N = 12,398; brown vs. black hair colour: N = 10,990; and red vs. brown + black hair colour: N = 10,450. Q-Q plots of the meta-analyses indicated no inflation of p values  of each of the GWAS on METASOFT v2.0.1p value ≤ 1.67e-08) overlapping or near genes known to affect normal pigmentation variation  15 . Other genome-wide significant loci overlapped SLC45A2 (Chr 5), IRF4 (Chr 6), TPCN2 (Chr 11), KITLG (Chr 12), SLC24A4 (Chr 14) and MC1R (Chr 16). Similarly, on the brown hair colour meta-analysis , which is in high LD with rs12913832. Other genome-wide significant regions overlap SLC45A2 (Chr 5) and IRF4 (Chr 6).Our meta-analyses identified genome-wide significant loci , a gene known for its loss-of-function mutations, which switches the production of eumelanin to pheomelanin45.On the red hair colour meta-analysis Fig. , the onlTYR, EDNRB, BNC2 and ASIP) which were not genome-wide significant in the meta-analyses using logistic mixed models, reached genome-wide significance here  was genome-wide significant. This gene has only been previously identified in the UKBB hair pigmentation study33. Given that we linearly regressed ordinal categories, we cannot assume that the differences between categories are equal. For this reason, and considering the similar results obtained with the logistic and linear mixed model approaches, we restricted downstream analyses to the meta-analyses based on the binary logistic mixed models.Our linear mixed model meta-analysis, in which hair colour categories ranged from blonde up to black (excluding red hair colour), yielded similar results as the logistic mixed models for blonde hair colour HERC2 locus is rs1129038, which is a SNP in high LD with rs12913832.For blonde hair colour, there was one SNP selected for each of the seven genome-wide significant loci . Notably, there were no overall differences in the results obtained with either matrices for blonde or brown hair colour, in respect to the independent SNPs per locus and overall summary statistics , and included their respective annotations with SNPNexus51 (See Methods for details). Based on the combined evidence of fine-mapping and annotation, we defined the candidate causal variants with strong evidence of causality as the most likely candidate causal variants. We have summarised the results for each hair colour model , which is the most likely candidate causal SNP in the locus, with high posterior probability  , the known missense SNP rs16891982 is present within the 95% credible set, although the SNP with the highest posterior probability is rs35391. On the SLC24A4 locus (Chr 14), the marker highlighted by GCTA-COJO (rs12896471) is among the most likely candidate causal SNPs. This marker is in perfect LD (r2 = 1) with a SNP previously associated with blonde hair colour (rs12896399)52, and both appear in the same 95% credible set.Bayesian fine-mapping analyses highlighted known functional pigmentation SNPs, in line with the results obtained with GCTA-COJO, such as rs12203592 within TPCN2 region (Chr 9), we identified the missense SNP rs3829241 as one of the most likely candidates of causality in the locus  with the missense SNP rs72928978. Another two known missense variants (rs3750965 and rs35264875) within TPCN2 appear in the same 95% credible set as rs3829241, but with a log10BF < 2, suggesting little evidence of causality.Different from GCTA-COJO, on the gion Chr , we idengion Chr , we idenHERC2/OCA2 region (Chr 15), the only SNP with considerable evidence of causality for blonde hair colour, based on its posterior probability and annotation, is the known regulatory SNP rs12913832 (PIP = 0.978). In contrast, there are two causal signals identified for brown hair colour highlighted by FINEMAP , in which one of them has log10BF > 2 (rs7168800) (Supplementary Table On the MC1R region (Chr 16) was associated with both blonde and red hair colour. FINEMAP highlighted three causal signals in the locus for blonde hair colour and ten causal signals for red hair colour. The credible sets include known missense SNPs within or near MC1R , in which rs1805005 had the highest PIP and log10BF on the blonde hair colour analysis   to explore the putative regulatory role of the SNPs identified in our hair colour GWAS and identify candidate genes . Additionally, we also observed colocalization of a GWAS signal with an eQTL of SLC24A4, with a regional probability = 1, but there was no candidate SNP selected, suggesting limited evidence of a single colocalized SNP between the traits. In addition, we identified five GWAS loci colocalized with meQTLs, associated with methylation of CpGs near MC1R, OCA2, IRF4, SLC45A2 and SLC24A4. Notably, we found GWAS colocalization with both eQTL and meQTL in three of these loci . In the case of SLC24A4, the SNP rs8022442 is a meQTL for CpG probes, cg11086312 and cg10004481. On the OCA2 locus, rs12913832 is a meQTL for CpG probes located upstream and downstream of the SNP within HERC2 . Furthermore, the SNP rs35391 is a meQTL for the CpG probes in the first exon of SLC45A2 region (cg14189614 and cg04302388).We conducted colocalization analyses of the GWAS meta-analyses genome-wide signals using hyprcolocMC1R for red hair colour. Given the current evidence, this is likely explained by the fact that known loss-of-function polymorphisms within MC1R lead to red hair colour, therefore, they have a direct functional role on the translated protein. However, there is a possibility that we might be missing eQTLs beyond the 500 kb tested region. Nonetheless, we did identify colocalizing meQTLs (rs258322) for blonde hair colour, associated with the CpG methylation near MC1R: on or near CDK10, GAS8 and DPEP1 , which may point at an independent regulatory region associated with blonde hair colour, apart from the known missense SNP within MC1R (rs1805005)39. Additionally, we observed a colocalized signal between GWAS and meQTL near IRF4 for both blonde and brown hair colour (cg23785612). Finally, neither of these loci  harbour corresponding eQTLs.We did not find colocalization of QTLs on or near the gene 54. Similar to the colocalization results, the decreased expression of OCA2 and SLC24A4 was significantly associated with blonde hair colour  using the GWAS summary statistics, and we imputed the expression profile based on the CanPath cohort LD, from the expression weights calculated from the cultured melanocytes RNA-seq dataur Table . In contRIN3 near SLC24A4 was also significantly associated with blonde hair colour. After performing conditional TWAS analysis to check if these two signals were independent from each other, we found that the gene RIN3 signal is not independent from that of the nearby gene SLC24A4 , as well as HERC2. It is well known that MC1R signalling reduces UV-induced DNA damage by mediating a cascade of reactions after the activation of cAMP-dependent Protein Kinase A (PKA)57. Likewise, loss-of-function mutations on MC1R diminish the UV-induced DNA damage repair57. HERC2 is known to be involved in DNA repair induced by UV58, but the expression of this gene likely does not play a role in hair colour variation, as the associated SNPs are within introns, serving as enhancers to regulate the expression of OCA2.Notably, the DNA repair process includes genes surrounding the 59. We assessed if our genome-wide significant signals associated with hair colour have also been significantly associated with sun tanning , melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), based on the databases available in the NHGRI-EBI GWAS Catalog60. We tested a total of 327 SNPs across all phenotypes , of which 21 unique SNPs overlap our GWAS hits . These SNPs are distributed across key pigmentation loci that overlap genes, such as SLC45A2, IRF4, SLC24A4, OCA2, HERC2 and MC1R 61. Additionally, we have incorporated colocalization analyses of expression and methylation QTLs, as well as TWAS, both of which provide insights on the regulatory mechanisms of hair colour variation and pigmentation biology.In this study, we conducted meta-analyses of genome-wide association studies of hair colour on a Canadian cohort of European ancestry. Furthermore, we performed several post-GWAS analyses to investigate in more detail the genetic architecture of this trait, by focusing on the genome-wide associated loci. By applying statistical fine-mapping methods, we identified pigmentation loci harbouring more than one causal signal with high confidence, similar to what has been previously observed33.We note that hair colour is a naturally quantitative trait, which shows substantial variation as a result of differences in the amount of melanin in the hair, as well as differences in the ratio between the two types of melanin, eumelanin and pheomelanin. In an ideal scenario, quantification of the phenotype on a large sample would yield more accurate association results given the nature of the trait. However, recent studies from large-scale biobanks have shown a drastic increase in power to detect significant associations when the sample sizes increase, even if a self-reported, qualitative approximation of hair colour is usedSLC45A2, KITLG, SLC24A4, OCA2, MC1R, among others. However, by using a linear GWAS model based on four eumelanin ordinal categories , we replicated the signal of the gene ARL15 that was previously identified in a recent UKBB study33 . It is known that TPCN2 encodes a cation transporter channel in melanosomes that regulates the pH of melanosomes to downregulate melanogenesis67. There are three nonsynonymous SNPs  associated with blonde hair colour reported in the GWAS literature52. Two of these nonsynonymous SNPs (rs3829241 and rs35264875) have been experimentally shown to modify the pH of the melanosome, an important factor for tyrosinase’s activity68.Our fine-mapping analyses highlighted several regions that harbour more than one causal signal. On the 69, may also have a functional effect on the TPC2 protein, as predicted by SIFT and Polyphen . In addition, the A-allele of rs72928978 shows high population differentiation: it is common only in European and European admixed populations of the 1KGP (mean MAF in EUR = 0.14) and it is absent from other continental populations of the 1KGP , both of which overlap histone marks identified in foreskin melanocytes  associated with enhancer signals70. This result contrasts to observations by Adhikari et al., in which they detect a secondary variant associated with eye and skin pigmentation on the HERC2/OCA2 locus, but not with hair colour5.We were particularly interested in studying in more detail the MC1R locus, some of which have high penetrance and are termed “R” variants  and others that have low penetrance and are termed “r” variants . Compared to “R” variants, which impair the function of the protein and lead to the synthesis of pheomelanin in a homozygote or compound heterozygote state, “r” variants only reduce the protein’s efficiency, leading to low levels of eumelanin synthesis71. We have identified here candidate causal R and r variants associated with red hair colour , but also with blonde hair colour . The SNP rs1805005 is one of the most likely candidate causal SNPs, although there is a high probability of additional causal signals in the region. In fact, a SNP within CDK10 (rs258322) is a colocalizing meQTL for blonde hair colour. The meQTL is in moderate LD with a candidate causal SNP with log10BF > 2 . These results provide insights about possible regulatory variants leading to blonde hair colour.The red hair colour phenotype is mainly a consequence of missense polymorphisms on the SLC24A5, SLC45A2 and SLC24A4), which are also involved in normal pigmentation variation. Light skin pigmentation in people of European ancestry is driven mainly by two nonsynonymous mutations (rs1426654 and rs16891982) on SLC24A5 and SLC45A2, respectively72. Notably, our colocalization analyses suggest that there may be additional SNPs in the SLC45A2 region regulating the expression of the gene.Within the solute carrier family, there are at least three transmembrane proteins involved in ion transport  with the largest effect (rs12896399), which is a common variant in all 1KGP continental populations, except in African populations. Based on the Genotype-Tissue Expression (GTEx) Project, rs12896399 is significantly associated with the expression of SLC24A4 in skin tissue . However, to the extent of our knowledge, there is no clear evidence of the molecular process by which this SNP regulates the expression of SLC24A4. By conducting colocalization and TWAS analyses, we identified both an eQTL and meQTL in the SLC24A4 locus, in which the candidate meQTL is in moderate LD with the fine-mapped candidate causal SNP rs12896471 (r2 = 0.65).Several reports have identified the association of MC1R, IRF4). These loci do not harbour colocalizing eQTLs, which suggests that other mechanisms may be involved, such as trans-QTLs76, which were not considered in the current analysis. Alternatively, it is possible that some CpG probes capture the status of poised enhancers , which may not yet have any influence on gene expression in actively growing melanocytes. This is a possible scenario, given that the melanocytes used in the QTL analyses were from newborns. However, none of the candidate colocalized SNPs in these loci (rs258322 and rs7773997) are eQTLs of MC1R and IRF4, respectively, in adult skin tissue based on the GTEx Project. Experimental histone modification marker assays may provide support for the alternative hypothesis, as it is known that poised enhancers lose H3K27me3 and acquire acetylation at the same amino acid residue upon activation77.Our colocalization results highlighted meQTLs for blonde hair colour, associated with the methylation of CpGs near known pigmentation genes  with pigmentation traits , as well as with the expression of genes.Several of the genome-wide significant SNPs we identified in the CanPath cohort overlap SNPs associated with different types of skin cancer and response to UV radiation, and it is particularly the case for melanoma. Additionally, a few of the overlapped SNPs are also colocalized eQTLs and/or meQTLs , which highlights the importance of investigating the genetic and epigenetic mechanisms involved in the pigmentation pathway, such as hair colour. In fact, a recent study has provided a first glance into the epigenetic mechanisms  of pigmentation genes mediating skin cancer using whole blood tissue78) on our colocalization results, but none of their SNPs was present in our colocalization results. The differences may lie in the fact that we used cultured melanocytes, which provide a cell-specific expression and methylation profile, best suited for the traits being tested76. However, it is relevant to note that the authors also successfully colocalized a DNAm site near the gene CDK10 with blonde hair colour, as well as with several skin cancer traits , which further reinforces the evidence we reported here, regarding putative regulatory variants in that locus.We followed-up their QTLs , Atlantic Coast Provinces  , British Columbia , Ontario , and Quebec . We selected individuals who self-reported having European-related ancestry and for whom self-reported hair colour was available. Among all participants included here, 53.78% were females and the average age was 53 years old (SE ± 7.85).This study was approved by the University of Toronto Ethics Committee (Human Research Protocol # 36429) and data access was granted by the Canadian Partnership for Tomorrow’s Health (Application number DAO-034431). All relevant ethical regulations were followed, and informed consent was obtained from CanPath participants. The samples in this study correspond to a subset of 12,996 individuals from the Canadian Partnership for Tomorrow’s Health (CanPath), which were sampled in different provinces: Alberta , (ii) Global Screening Array  24v1 (N = 438), (iii) 24v2 + MDP , (iv) 24v1 + MDP , and (v) Omni 2.5 (Illumina) (N = 526) by the Canadian Partnership for Tomorrow’s Health (CanPath) project. The number of single-nucleotide polymorphisms (SNPs) of these chip arrays ranges between 626,377 and 2,349,746 SNPs.An overview of the methodological workflow is shown in Supplementary Fig. 79. Afterwards, we used PLINK (version 1.9)81 to filter out variants with minor allele frequency <1%, high missing genotyping rate (–geno 0.05), high missing individual rate (–mind 0.05) or variants that significantly deviated from the Hardy-Weinberg Equilibrium (HWE) (–hwe 1e-06). Then, we also identified second-degree relatives  using a pruned set of variants in linkage disequilibrium (LD) (–indep-pairwise 100 10 0.1), and filtered out, from each pair, the individual with the lowest genotyping rate. We performed a Principal Components Analysis (PCA) of a pruned set of common variants of our study samples projected on the 1KGP European Phase 3 samples on PLINK (version 1.9)81 , GC/TA variants with minor allele frequency >0.4 in the 1KGP-EUR and flipping alleles according to the 1KGP-EUR, using a Perl script (version 4.2)N = 4,745), (ii) GSA 24v1 , (iii) GSA 24v2 + MDP , (iv) GSA 24v1 + MDP , and (v) Omni 2.5 .  and individuals (N) remaining after quality control for each chip array were: (i) Axiom 2.0 UK Biobank  algorithm84 and the Haplotype Reference Consortium (HRC) release 1.1 dataset as reference83. The HRC includes ~64,000 haplotypes and ~40,000,000 autosomal SNPs of ~32,000 individuals predominantly of European ancestry, which makes it ideal for the imputation of our datasets, which are of European-related ancestry. Post-imputation quality control consisted of filtering out variants with INFO score <0.3. Briefly, The INFO score is a measure of the imputation certainty across samples, in which INFO = 1 indicates complete certainty. We also filtered out variants with MAF <0.01, missing genotyping rate >5% or variants that significantly deviated from the HWE. The final number of markers included in the GWAS for each genotyping array were: (i) Axiom 2.0 UK Biobank = 6,880,138, (ii) GSA 24v1 = 6,185,935, (iii) GSA 24v2 + MDP = 6,214,597, (iv) GSA 24v1 + MDP = 6,204,261, and (v) Omni 2.5 = 7,391,256.Each genotyping array was first phased with EAGLE2 (version 2.0.5)85 to build logistic mixed models to compare the presence (1) or absence (0) of: 1) blonde vs. brown (light and dark) + black hair colour; 2) brown (light and dark) vs. black hair colour; and 3) red vs. brown (light and dark) + black hair colour, similar to the approach used by Morgan and colleagues33. Supplementary Table Participants of the CanPath answered a questionnaire that included self-report on natural hair colour (before greying) using the following discrete categories: black, dark brown, light brown, blonde, red hair colour or NA. These categories were then transformed into binary categories using R (version 3.5.1)41, using an additive genetic model , and considering the genotypes’ dosages. Specifically, the three hair colour models used were: 1) blonde vs. brown (light and dark) + black hair colour; 2) brown (light and dark) vs. black hair colour; and 3) red vs. brown (light and dark) + black hair colour. We performed a PCA of a pruned set of genotyped variants for each genotyping array after quality control, keeping only SNPs with MAF > 0.05 and excluding regions of high LD, using PLINK (version 1.9)81. We included 10 PCs as fixed effects in the logistic mixed models for all genotyping arrays. We also included in the model sex and age as fixed effects. Additionally, we included as random effects a genetic relationship matrix of independent markers to account for subtle structure, computed on PLINK (version 1.9)81. We did not perform a GWAS for the presence/absence of red hair colour in the two small samples (Omni 2.5 and GSA 24v1) due to the low number of cases (N < 20). To evaluate the case of residual population substructure, we computed the inflation factor (λ) and visualised the expected vs. observed p values using Q-Q plots on R (version 3.5.1)85.Genome-wide association studies of hair colour were performed for each genotyping array with binary logistic mixed models on SAIGE (version 0.38)42. METASOFT conducts a meta-analysis using a fixed-effects model (FE), which works well when there is no evidence of heterogeneity , and an optimised random-effects model (RE2), which works well when there is evidence of heterogeneity among studies42. Additionally, METASOFT computes two estimates of statistical heterogeneity, Cochran’s Q statistic and I2 statistic86. We considered a SNP as heterogeneous across studies at an alpha level of 0.05 and K-1 degrees of freedom, where K is the number of studies included in the meta-analysis. Similarly, values of I2 > 50% are considered to represent notable heterogeneity87.We performed a meta-analysis for each hair colour model  using the beta coefficient (i.e. log(odds ratio)) and standard error (SE) of each study on the software METASOFT (version 2.0.1)88 and ggplot289 R packages. We used a genome-wide significant threshold of 1.67e-08  to account for the three models tested. We focused our results on the fixed-effects model, but we also report the RE2 on the summary statistics of the top signals as Supplementary Data 2 statistics. We performed LD Score regression on LDSC90 (version 1.0.1) on each of the meta-analyses summary statistics to evaluate possible inflation, using the LD Scores from the European population of the 1000 Genomes Project.After conducting the meta-analyses results, we generated Manhattan and Q-Q plots using the qqman32. We coded the hair colour categories as a quantitative trait, spanning from low to high eumelanin . For this analysis, we excluded red hair colour, given that red hair colour is characterised by its high levels of pheomelanin, and does not fall within the low to high eumelanin spectrum91. We performed the GWAS on GCTA 1.26.093, including as fixed-effects sex, age and the first 10 PCs and a genetic relationship matrix as random effects. Subsequently, we performed a meta-analysis on METASOFT (version 2.0.1)42. We generated Manhattan plots for the meta-analysis results.Additionally, we conducted GWAS and meta-analyses of hair colour using a linear mixed model approach, similar to the methods used in previous hair pigmentation studies51 to annotate the genome-wide significant signals  from each meta-analysis. Specifically, gene and variant type annotation were done using the University of California Santa Cruz (UCSC) and Ensembl databases (human genome version hg19); assessment of the predictive effect of nonsynonymous coding variants on protein function was done with SIFT and PolyPhen scores. Both SIFT and PolyPhen output qualitative prediction scores . Noncoding variation scoring was assessed using CADD score, which is based on ranking a variant relative to all possible substitutions of the human genome. In addition, we explored the effect of significant loci on RNA and protein expression using the GTEx database94 and the effect of significant genes using the Protein Atlas95.We used the web-based programme SNPnexus46. We performed the analysis (–cojo-slct) using as input the summary statistics of our hair colour meta-analyses  and the weighted average effect allele frequency from all studies. In addition, the programme requires a reference sample for computing LD correlations and, in the case of a meta-analysis, it is suggested to use one of the study’s large samples46. Therefore, we ran the analysis twice: 1) using as a reference the sample genotyped with the Axiom UKBB array (N = 4745), and 2) using as a reference the sample genotyped with the GSA 24v1 + MDP (N = 4480), including in both cases only high imputation-score SNPs (INFO > 0.8). We assumed that variants farther than 10 Mb are in complete linkage equilibrium, hence we ran the programme on each chromosome separately to speed up the analyses and used the genome-wide significant p-value threshold of 1.67e-08.In order to identify if the genome-wide significant loci of our original logistic meta-analyses were driven by one or more independent variants, we conducted approximate conditional and joint analyses of association (COJO) with the programme Genome-Wide Complex Trait Analysis (GCTA)49 to identify candidate causal variants in the genome-wide associated loci across the genome for each binary phenotype. FINEMAP is based on a Bayesian framework, which uses summary statistics and LD correlations among variants to compute the posterior probabilities of causal variants, with a shotgun stochastic search algorithm49. Compared to other methods, FINEMAP allows a maximum of 20 causal variants per locus. To run the programme, we used as input the meta-analyses summary statistics of each binary phenotype, including the weighted average MAF among all studies, and an LD correlation matrix from one of the large samples in our study . The LD correlation matrix was computed using LDStore (version 2.0), which considers genotype probabilities96. We defined regions for fine-mapping as ±500 kb regions flanking the lead SNP, based on the genome-wide signals of association from the meta-analyses, and allowing a maximum number of 10 causal signals for each locus . A credible set is comprised of SNPs that cumulatively reach a probability of at least 95%. The SNPs within a credible set are referred to as candidate causal variants and each of them has a corresponding posterior inclusion probability (PIP).We used the programme FINEMAP (version 1.4)10BF < 2 from each of the 95% credible sets, where a log10BF indicates considerable evidence of causality. We annotated the remaining SNPs using SNPnexus51 to obtain information about the overlapping/nearest genes, overlapping regulatory elements and CADD scores. Annotation of gene expression on ENCODE, Roadmap Epigenomics and Ensembl Regulatory Build was restricted to melanocytes, keratinocytes and fibroblasts, which are the relevant cell types involved in hair pigmentation. Based on the combined evidence of fine-mapping and posterior annotation, we defined the candidate causal variants with strong evidence of causality (based on their log10BF and annotation) as the most likely candidate causal variants. We computed LD correlations among the candidate causal SNP(s) on each locus using LDStore (version 2.0) and plotted the Posterior Inclusion Probability (PIP) and log10BF results on R (version 3.5.1)85 using ggplot289.We filtered FINEMAP results by removing candidate causal variants with a logcis-QTL data from primary cultures of foreskin melanocytes, isolated from the foreskin of 106 newborn males76. Cis-QTLs were assessed for variants in the ±1 Mb region of each gene or CpG76. Foreskin melanocytes are currently the most adequate choice to study regulatory mechanisms involved in hair colour due to the shared pigmentation pathways in skin and hair. We used the programme hyprcoloc53 to obtain the posterior probability of a variant being shared between the GWAS and the expression or methylation QTLs. We tested all the significant eQTL genes or meQTL probes within ±250 kb regions flanking the most significant GWAS SNP on each of the genome-wide regions of association  from the logistic meta-analyses summary statistics (11 different loci across the three GWAS models). We used as LD reference the matrix obtained from the CanPath’s Axiom UKBB Array (INFO score >0.3), computed on PLINK 81. We kept and report colocalized regions that reached a posterior probability ≥0.8, indicating high confidence of shared signal.We conducted colocalization analyses of our GWAS meta-analyses signals using gene expression and methylation 54. Using the programme FUSION37, we used as LD reference the CanPath’s Axiom UKBB genotyping array computed in binary PLINK format 81. As recommended by FUSION, we used the LDSC munge_sumstats.py script to check the GWAS summary statistics90. Before running the script, we filtered out SNPs with MAF < 0.01, SNPs with a genotyping missing rate >0.01 and SNPs that failed HWE test at significance threshold of 10e-07 using PLINK 81. We computed expression weights from our melanocyte RNA-seq data one gene at a time. Genes that failed quality control during a heritability check  were excluded from the further analyses, yielding a total of 3998 genes. We restricted the locus to 500 kb on either side of the gene boundary. We applied a significance cut-off to the final TWAS result of 4.17e-06 (i.e. 0.05/(3998 genes x 3 models tested)). Finally, we performed conditional analysis on FUSION (FUSION_post.process.R script) if more than one gene in a locus was significant, to identify if these were independent signals. We also ran a follow-up permutation test with a maximum of 100,000 permutations to assess if the random distribution of QTL effect sizes could yield a significant association by chance.We performed three transcriptome-wide association studies (TWAS) by imputing the expression profile of the CanPath cohort using GWAS summary statistics and melanocyte RNA-seq expression data97, to annotate relevant gene sets in a biological context. FUMA tests if genes are overrepresented in any pre-defined Gene Ontology (GO) gene set, and the significance (p < 0.05) is adjusted for multiple testing per biological category separately, using the Bonferroni method. We used as input the gene IDs that overlapped candidate causal variants defined by FINEMAP for all models jointly (Supplementary Table We performed pathway enrichment analysis using the GENE2FUNC application available on the web-based programme FUMAStatistical analyses were performed for the GWAS and downstream analyses as described in the corresponding Methods section, including all parameters used to allow reproducibility.Further information on research design is available in the Transparent Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary DataReporting Summary


Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% – 23% higher than corrected estimates using UK Biobank data. Methods for estimating heritability, the fraction of variance attributable to genetic factors, assume random mating. Here, the authors show that under assortative mating, when mate choice reflects phenotypic similarity, these methods produce overestimates. A century ago, Fisher demonstrated that AM induces long-range positive correlations between trait-increasing allele counts at causal loci across the genome, thereby increasing genetic variance across successive generations until it approaches a stable equilibrium5. Since Fisher’s time, it has been established that many human traits are subject to AM, and that estimates of genetic and environmental variance from twin and family designs, which assume random mating, can be biased in the presence of AM6. Recently, there has been increased focus on how AM can influence results based on measured genetic data, in part due to the recognition that AM may contribute to observed discrepancies between population-based versus family-based estimates in genome-wide association studies (GWAS)10 and Mendelian randomization studies12. However, despite hundreds of publications reporting estimates of SNP-heritability, no study has yet investigated how AM influences these estimates. Moreover, many benchmark traits central to the scientific discourse regarding the “still-missing heritability”—for example, height and educational attainment—are precisely those for which phenotypic and genetic data is consistent with AM15, raising the possibility that conclusions about SNP-heritability could be biased in systematic but as yet poorly understood ways.Positive primary phenotypic assortative mating (hereafter simply “AM”), the phenomenon whereby mate-choice is based on phenotypic similarity, has been observed for a variety of heritable traits in human and non-human animals16 but includes PCGC regression17 and linkage disequilibrium (LD) score regression18. The second uses residual maximum likelihood (REML)19 to estimate heritability  under AM, which clarifies why genomic principal components fail to capture the effects of AM. Additionally, we provide empirical results using data from the UK Biobank that are congruent with our theoretical predictions. Finally, we provide guidelines for estimating and interpreting Here, we characterize the impact of AM on the two families of marker-based heritability estimators, which we generically denote Our theoretical results depend on several key parameters: The rows of mentclass2pt{minimuTu Fig.  1, 2.FigWe first characterize the influence of AM on 22 is evidence for phenotypic assortment, but the actual increase is trivial , we derive the following approximate expression, dependent only on Under exchangeable loci for known 24, is similarly biased upwards under AM  with mentclass2pt{minim AM Fig. . HoweverIn contrast to h∞2 Fig. . More fomentclass2pt{minimh02 Fig. .Inclusion of ancestral PCs as covariates fails to mitigate the AM-induced bias in both the MoM and the REML estimates Fig. . Indeed,26; these circumstances are further complicated under AM as imperfect tagging will not only corrupt the relationship between a given marker and proximal causal variants, but between a given marker and all other causal variants. To assess the impact of AM when not all variants are measured, we compared π = 0, 50, or 75% of all In real-world applications, measured genotypes will often include only a fraction of the total number of causal variants (or at least good proxies in high LD). Previous work has demonstrated that the extent to which marker data imperfectly tag causal variants will bias NPs Fig. . As expe27. We a priori selected four phenotypes based on evidence  or lack of evidence  for primary phenotypic AM in a previous study14. We then computed 28. Unlike previous approaches which quantified the degree of AM for traits by correlating polygenic scores between mates4 or within-individuals but between chromosomes14, our approach does not require the calculation of polygenic scores and is agnostic with respect to variant direction and effect size. Thus, calculating To investigate whether our theoretical predictions are observable in real data, we examined the relationship between sample size and 2 and educational attainment4 into Eq.  and the conditional independence of mates’ genotypes given the heritable components of their phenotypes (which may be violated in structured populations), are more difficult to evaluate and deserve consideration in future investigations. For instance, when environmental influences are transmitted from parents to offspring, the dependence between causal variants will extend to non-heritable factors such that, in subsequent generations, all trait-increasing allele-counts and trait-increasing environmental factors will become positively correlated, and the linear mixed model assumed by both the REML and MoM estimators will be further misspecified.10), as was pointed out in ref. 38. This bias is different than those described here in that it is lower than either the base population or equilibrium population heritability. Additionally, we have not found closed-form solutions to We have also not investigated the impact that AM has on polygenic scores or within-family classes of estimators. For example, LD-score regression can be estimated based upon regression estimates from within-sibling analyses. The estimated genetic variance from such an analysis would be that in the base population (because it is based on the segregation variance), but the estimated phenotypic variance would be that in in the current population. This would also lead to a bias in heritability estimation , at which point the within-individual and cross-mate correlations among causal variants are equal to one another. Using the results of Nagylakimentclass2pt{minim41, to the case of random matrices with independent rows meeting particular moment conditions  reference panel49. Genotype data were then phased to the 1KG3 reference panel in batches of 40,000 individuals using Eagle v2.450. Data were then grown to a population of 6 chimeric genotypes and subjected to an additional five generations of random mating as described above before being subject to AM.For each simulation, the input data were derived from phased, imputed genotypes at 20, resulting in an average sample size of 141,667 across simulated datasets. Additionally, we ran a limited number of larger, more computationally intensive simulations (6) with mating correlations fixed at We conducted AM simulations for varying mating correlations, mentclass2pt{minim20 to construct genomic related matrices and perform HE regression. We obtained 51 for computational efficiency; though BOLT-LMM uses a randomized algorithm, its numerical accuracy is comparable to that of the exact algorithm implemented GCTA52.We split each simulated genotype–phenotype dataset into collections of random subsamples mutually exclusive within collection but not across collections, yielding 16 samples of 16,000 individuals, eight samples of 16,000, four samples of 32,000 individuals, two samples of 64,000 individuals, and one sample of 128,000 individuals. We then performed HE regression and single-component REML for each subsample Fig. . We used53, as the HE regression implementation in GCTA cannot accommodate covariates. To demonstrate that the behavior of LDSC under AM is equivalent to that of HE regression (assuming that the LD scores accurately reflect the LD structure of the sample), we used PLINK v1.954 to obtain GWAS summary statistics and LDSC v1.0.118 to estimate within-sample LD scores using a one centiMorgan sliding window and to perform LDSC regression  do not mitigate the impact of AM, we fit multicomponent HE regression and REML after partitioning SNPs by minor allele frequency and LD score . We used LDAK v5.0 to obtain adjusted HE regression estimates53. In order to quantify the divergence of We tested for evidence of AM by comparing Sc its complementary small subsample. Though this procedure accounts for the dependence among estimates derived in the same subsamples, the individual observations were derived from various partitionings of the same data and do not constitute truly independent observations. Therefore, the 57 using the ggplot2 v3.3.358 and MASS v7.3.4959 libraries.All statistical analyses and visualizations were conducted in R v3.5.0Ethics approval for the UK Biobank study was obtained by the UK Biobank team from the North West Center for Research Ethics Committee (11/NW/0382). Access to the UK Biobank data was granted to principal investigator Dr. Matthew C. Keller (researcher ID 16651).Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning  and deep learning  together with the four common methods  were conducted for two main disease resistance traits  in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs  were somewhat lower (0.3–15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms ( Examples using simulated and real animal and plant data showed that the prediction accuracies (r) using machine learning were 10% greater than linear  and 1.3% greater than nonlinear methods , for instance, r = 0.732–0.758 for binary and continuous traits using GBLUP vs 0.801–0.832  increased 20–70% the prediction accuracy for disease resistance to photobacterium in gilthead sea bream  and its extension known as single-step GBLUP (ssGBLUP) to nonlinear Bayesian approaches   in Asian seabass  and to make comparisons with four common methods . In addition, we explored if imputation of missing genotypes, multiple traits analyses and significant genome-wide markers could improve the prediction accuracies for the disease-resistant traits. Our results open new opportunities for genome-based selection to increase the animal resistance to E. ictaluri in striped catfish as well as other aquaculture species infected by this highly infectious pathogen.Therefore, the principal aim of this study was to assess genomic prediction accuracies of machine and deep learning methods for two main disease resistance traits .E. ictaluri  for the challenge test using cohabitant method . Two days after the injection, they were released into the cement tanks to rear with the experimental fish with a ratio of 1–3 (or roughly 30% cohabitant fish in each tank). The bacteria were added to the experimental tanks at day 4 at a dose of 105 CFU/ml to retain the bacterial density for disease infection. The experiment was conducted over a period of 23 days when no death fish was recorded. During this period, the feeding rate was reduced to 1.5% of the total biomass in tank. Mortality was highest in day 5 and dead fish were sampled for laboratory PCR test to verify that their death symptoms  were due to E. ictaluri pathogen. At the conclusion of the experiment, all alive fish were biosecure-buried, following the regulations of the national veterinary authority .The challenge test involved a total of 5328 individuals from 166 families . The experimental fish were initially acclimatized in cement tanks for about 2 weeks. Then the same number of fish from each family was randomly allocated to six cement tanks  and alive animals at the end of the challenge test were assigned a number 1. Furthermore, survival time was defined as the continuous trait from the start of test until the animal death in day. Both survival status and survival time were analyzed using linear mixed model by ASReml 4.1  fitted in the model. The linear mixed model comprised the fixed effects of spawning batch (7 levels) and challenge tanks (2 levels) and a covariate of age from birth to tagging. Estimated heritability and common full-sib effect obtained from this dataset using the above linear mixed models were 0.09 ± 0.06 and 0.06 ± 0.03 for survival status and 0.11 ± 0.08 and 0.13 ± 0.04 for survival time. Based on the EBVs ranking, 20 highest resistance families and 20 lowest resistance families were chosen from 166 families. Only families with the number of offspring greater than 9 were selected to ensure that the EBVs were estimated with a high level of reliability. Next, we randomly collected fin tissue samples of 12–15 fish per family per disease resistance group for genome sequencing using Diversity Arrays Technology (DArTseqTM).During the challenge test, death fish were collected every 3 h and their clinical symptoms were also recorded. The data were used to calculate two measures of TM technology. The DArTseqTM was based on the genome complexity reduction method in combination with high throughput next-generation sequencing using Illumina platform. The sequencing protocol including choice of restricted enzymes and library preparation was optimized for striped catfish (Pangasianodon DarTseq 1.0) using 96 independent DNA samples of striped catfish in our earlier study  were sent to a commercial service provider in Canberra, Australia for genotyping by sequencing using DArTseqer study . The avei.e., pedigree) information. Specifically, this method used parental genotypes to impute the missing values of offspring genotypes and then, the offspring genotypes were used to fulfill the missing values of their parents (The missing genotypes were about 10.0% in this study. They were imputed using AlphaFamImpute (h2) was calculated as a ratio of the additive genetic variance (c2) was not significant (P > 0.05) and hence, this effect was omitted from the statistical models used to estimate genetic variances and prediction accuracy.Variances components of the two studied traits  were estimated using linear mixed model under the best linear unbiased prediction (BLUP) framework in AIREMLf90 sub-program of the BLUPF90 family package , and deep learning .Accuracies of genomic prediction for the disease resistance traits  were estimated using BLUP-family methods , BayesR, machine learning , b is the vector of fixed effects , a is the vector of the random term . X and Z are the incidence matrices related to the fixed and random effects. The letter e refers to residual variance or error of the estimates corresponded to each phenotypic value.The mixed model used in PBLUP, GBLUP, and ssGBLUP is written in a matrix notation as follows:A, G, or H) used to solve the mixed model Equation (1) above.The main difference among BLUP-family methods  relates to relationship matrices  were implemented in BLUPF90 package  known asiβ follow a four-component normal mixture and the effect of SNP i is assumed to be distributed as:i.e., the cumulative variance of all SNP effects) and 2 distribution.BayesR assumes that SNP effects https://github.com/syntheke/BayesR) was run for 50,000 steps with 20,000 steps discarded as burn-in. We used predicted phenotypes (computed in AIREMLf90 using Equation 1), as this package does not allow fitting covariates in the model. Also, genotypes executed in BayesR were converted using PLINK software  as covariates and a SNP-weighted trait-specific kinship matrix as the (co)-variance assumption corresponding to the random effect. The selections of QTNs and SNP weights are optimized by machine learning. In a matrix notation, Equation 1 becomes:Q is covariates matrix of QTNs detected throughout multiple regression analyses; q is a vector of fixed effect of each QTN corresponding to Q and Kw is SNP-weighted Kinship that is optimized by grid search and bisection procedure. The implementation of KAML included two main steps, i.e., define Q and Kw and then predicting genomic breeding values of the validation set. In the training step, KAML optimized parameters for determining QTNs and SNPs weights which used all testing subset data provided throughout cross-validation. Next, in the prediction step, the optimized parameters were used to predict genomic breeding values of each individual in validation set. A detailed description of the step-wise procedures and the underlying algorithms of KAML is given in https://github.com/YinLiLin/KAML) in R environment  for testing sets and masked  phenotypes of validation sets. To define the best parameters  for mixed models of each testing set, we used fivefold cross-validation with 100 replications .KAML is a machine learning-based method that incorporates cross-validation, multiple regression, grid search, and bisection algorithm to improve genomic prediction accuracy for complex quantitative traits (ironment . KAML uthttps://github.com/lauzingaretti/deepGP). A detailed description of the theoretical framework is given in For deep learning analysis, we used DeepGP package . Each of SNP information in the input layer is transferred its value to the first neuron of the first hidden layer by the Z function:W is weighted SNP information, f(x) is nonlinear function and b is bias . In the case of genomic prediction, the input layer is SNPs genotype  of individual fully connected to the first hidden layers. As a result, the value in the first neuron of the first hidden layer is the sum effect of Z function over 6470 SNPs  and hyperparameters which related to https://github.com/lauzingaretti/DeepGP) were used to obtain hyperparameters in our DL-MLP analyses.Multilayer perceptron is fully connected networks consisting of an input layer, one or several hidden layers, and an output layer. In the context of genomic prediction, input layer receives SNPs data . Then, the pooling step is responsible to reduce spatial size of SNPs data of the convolutional layer into the second convolutional layer. There are two types of pooling such as max pooling and average pooling corresponding to maximum values and mean values of SNPs portion covered by the kernel. The process was repeated to make update value of the last convolutional layer. At the last convolutional layer, data were flattened . The fully connected layer works similarly the MLP. The hyperparameters used in our study are size of kernel (–ks 3), learning rate (–lr 0.025 0.01), dropout in the first layer (–dr1 0 0.01), dropout in the hidden layer (–dr2 0 0.1 0.2), number of pooling size (–ps 1), number of convolutional layers (–ncov 1 3), number of strides (–ns 1), number of convolutional operations (–nfilters 8). These hyperparameters are fine-tuned under DeepGP with grid search provided in DeepGP package  convolutional layers, (2) fully connected layers, and (3) input layers. In the context of genomic prediction, first convolutional layer receives data of 6470 SNPs and write it to the first layer using kernel  for both disease resistance traits. Theoretically, there were seven steps in performing ssGWAS in BLUPF90 program . These SNP panels were used to test the predictive performance of six genomic prediction methods used in our study.Single step genome-wide association analysis (ssGWAS) was accomplished in BLUPF90 suite using each subset phenotype data  in BLUPF90 family program, BayesR, ML-KAML, DL-MLP, and DL-CNN.The predictive performances of all seven methods  used in this study were evaluated using repeated fivefold cross validation. The procedure involved a random partitioning of the full data into 5 subsets . The phenotypes of one subset were masked and GEBV/EBV  of this set were predicted using the information from the other four subsets. This means one phenotype was used once in the validation process of one repetition. Each analysis was repeated 5 times, giving 25 validation sets in total and the correlation coefficient was averaged over 25 values. To compute evaluation metric in each validation set, the GEBV/EBVs of the predicted phenotypes were correlated to the actual phenotype. We chose Pearson correlation coefficient  for bina package  to produThe genomic prediction accuracy for survival status and survival time was calculated as R2 (coefficient of determination) based on 2 were calculated as below:n actual phenotypes in a testing set.To evaluate reliability  of the statistical methods, we computed root mean square error (RMSE) and E. ictaluri pathogen, mortality occurred after 1.4 days and the highest death rate was observed after 4.1 days  and survival time (5.0 vs 8.7 days) between 20 highest and 20 lowest resistant families  were greater than those obtained from (ss)GBLUP (0.44 ± 0.09 and 0.46 ± 0.09) but they were lower than those estimated by BayesR (0.97 ± 0.02 and 0.96 ± 0.03) . Across i.e., un-imputed data) were used, the prediction accuracies from ML-KAML (0.67) were slightly higher than PBLUP (0.66) and they both were higher than those calculated from GBLUP (0.55) and ssGBLUP (0.55) for survival status. ML-KAML also outperformed BLUP methods for survival time (0.69 vs 0.59–0.65) . For both traits, ssGBLUP was slightly better GBLUP. Interestingly, both GBLUP and ssGBLUP did not improve the prediction accuracies for both survival status and survival time as compared with conventional PBLUP method.59–0.65) . In addiImputation of missing genotypes increased the accuracies of genomic prediction for both survival status and survival time across the four methods used, namely GBLUP, ssGBLUP, BayesR, and ML-KAML . The impOur multivariate analyses used PBLUP, GBLUP, and ssGBLUP, and the prediction accuracies for both survival status and survival time are shown in P < 0.00001 in this study scarified the prediction accuracies by 0.3–15.6%. For survival status, the prediction accuracy was reduced from 4.3% to 6.0% for ML-KAML and BLUP methods, but greater loss was observed for deep learning methods (11.2% for DL-CNN and 15.6% for DL-MLP). The same trend was observed for survival time but the reduction in the prediction accuracy was only 0.3–4.9%.The prediction accuracies for the disease resistance traits that incorporated only significant SNPs identified from genome-wide association analysis (GWAS) are given in R2) were computed for each method and they are shown in vs subsets), the machine learning  gave the least possible biases in the breeding values prediction, as evidenced by the low RMSE  and high R2 (0.35–0.39). BayesR also displayed similar predictive reliability to ML-KAML . Among BLUP family methods, there were no large differences in the reliability of the genomic breeding value estimation between GBLUP and ssGBLUP . However, they were likely less reliable than PBLUP (RMSE = 0.39 and R2 = 0.30). The utilization of the imputed genotypes in GBLUP and ssGBLUP methods reduced the prediction errors for both survival status and survival time .The RMSE and coefficient of determination  and deep learning (DL-MLP and DL-CNN) methods were used relative to BLUP-family methods . Interestingly, PBLUP outperformed (ss)GBLUP methods in our study  to PBLUP, GBLUP and ssGBLUP, they had similar predictive power to BayesR for both traits in our study. Reports in farmed animals concluded that BayesR is the best amongst Bayesian and GBLUP methods  outperformed PBLUP and (ss)GBLUP  in Bay scallop indicated this method outperformed BayesB and random regression GBLUP methods  are analyzed for this population in the future.First, the genomic prediction accuracies for the disease resistance traits  were substantially improved when machine GBLUP  that appe.g., Beagle   and body traits in Atlantic salmon  increased the predictive ability of all the seven methods used, especially BayesR by 5.3%–approximately 19% (GBLUP and ssGBLUP) relative to when un-imputed data were used. Also note that the imputation of missing genotypes in our study differed from others that used software to compute missing genotypes without considering parental relationship or shared haplotype in full-sib families, , Beagle  or Alphagr) between the two traits (gr = 0.81). A bivariate analysis of harvest body weight and fillet yield in Nile tilapia also showed no improvement in the prediction accuracies for these traits  or genetically antagonistic associated . Examples of multivariate genomic prediction models involving multi-trait in cassava and wheat plants gave better prediction accuracy over univariate models  improved the prediction accuracy, for instance, about 5–19% for fertility traits in dairy cattle , likely due to the family structure of the current population that included selective genotypes (high and low resistant families). However, this problem may have been alleviated because both pedigree and genotype data were included in mixed models  to estimate genomic breeding values as described in Pangasianodon hypophthalmus.In summary, despite the high level of consistency in the prediction accuracies across the seven methods used, the estimates of heritability and prediction accuracies obtained for survival status and survival time may be potentially biased upward , original dart-seq genotypes, and pedigree information.All data are available via Figshare portal at


Ample nitrogen (N) is required for potato production, but its use efficiency is low. N supply strongly interacts with maturity type of the cultivar grown. We assessed whether variation among 189 cultivars grown with 75 or 185 kg available N/ha in 2 years would allow detecting quantitative trait loci (QTLs) for relevant traits. Using phenotypic data, we estimated various traits and carried out a genome-wide association study (GWAS) with kinship correction. Twenty-four traits and 10,747 markers based on single-nucleotide polymorphisms from a 20K Infinium array for 169 cultivars were combined in the analysis. N level affected most traits and their interrelations and influenced the detection of marker–trait associations; some were N-dependent, others were detected at both N levels. Ninety percent of the latter accumulated on a hotspot on Chromosome 5. Chromosomes 2 and 4 also contained regions with multiple associations. After correcting for maturity, the number of QTLs detected was much lower, especially of those common to both N levels; however, interestingly, the region on Chromosome 2 accumulated several QTLs. There is scope for marker-assisted selection for maturity, with the main purpose of improving characteristics within a narrow range of maturity types, in order to break the strong links between maturity type and traits like N use efficiency. In potato cropping, farmers often abundantly apply nitrogen (N) fertilizer to ensure profits because potato plants are highly responsive to extra N . This prEffects of nitrogen availability on above-ground and below-ground crop development have been widely studied. High N input increases individual leaf size and leaf longevity ,5 and prAn increase in nitrate available for the plant was reported to lead not only to a reduction in the proportion of dry matter allocated to roots but also to an increase in the total root surface and root length . DiffereStudies on canopy cover have shown a high correlation among the ability of genotypes to intercept photosynthetically active radiation, to change resource use efficiency, and to create tuber yield ,14,15. KIn recent years, association mapping approaches have become increasingly popular for genetic studies, offering a series of advantages that include higher mapping resolution and results that are applicable to a wider genetic background . Associat-test. Malosetti et al. , and DM%, at low N than at high N. At high N, NUpt, Vx, and TbwMx were closer to yield. Furthermore, there were five traits clustering together under both N conditions. This group included all traits from tuber size-weight and size-number distribution but not TbwMx  was highly positively correlated with yield at both N levels. More N input promoted vegetative growth and prolonged the growing period, and both effects may support higher yields, provided the growing season is long enough for the potato tuber yield to benefit from the prolonged canopy development. Lastly, the tuber size with maximum weight (TbwB) was significantly affected by N but not by Mt, and this trait is a representative of a group of traits that behaved consistently different than other traits included in the analysis at both N levels  values higher than 4 and explained at least 10% of the variance. The results of the association mapping are presented as marker–trait associations to generally describe the output of the analysis, to have an overall impression of the N level effect on the detection of associations in our dataset and to assess the colocalization of association with markers related to maturity. QTLs were defined using a linkage disequilibrium window of 8 Mb as mentioned in The association mapping was performed with kinship correction to minimize false positive associations. The marker–trait associations reported here had −logAn overall summary is shown in AP1, AP2, AUC, mt_as, [N], t1, t2, t2–t1, te, te–t2, and Y_DM). On Chromosome 2, there was another region accumulating associations for six traits , while there was a region on Chromosome 3 with markers associated with mt_as, TbnA, TbnB, TbnMx, and TbwB . A QTL for maturity assessment (mt_as) in our experiment was detected in the region on Chromosome 5, reported as maturity-related in the literature ,39,40,41and TbwB .AP2, AUC, [N], t2, te, and te–t2) were located on Chromosome 5. The other two traits were SCYi  and TbnB (with a QTL on Chromosome 3).Trait associations detected at both N levels  on ChromAP1, SCYi, t1, t2–t1 TbnMx, TbwA, TbwMx, tm1, and Y_DM) did not have QTLs colocalizing with maturity assessment , all within the LD window of 8 Mb. Eleven traits with 24 QTLs were exclusively detected at high N , and ninsessment .t2–t1, TbnA, TbnB, TbnMx, TbwB, and TbwMx) showed a total of 11 QTLs with markers not colocalizing with mt_as , effectively equalizing the trait means per maturity group. The relative differences between genotypes were maintained within a maturity group but corrected when comparing genotypes across maturity groups.The total number of associations detected with the phenotypic data corrected for the maturity main effects (CD) was 348, with 181 markers B, much lte–t2, and [N]), while 14 QTLs were N-dependent with seven QTLs at high N  and seven QTLs at low N . There were regions accumulating QTLs for three or more traits on Chromosomes 3, 4, and 12.There were 24 QTLs for 11 traits commonly detected with both datasets (CD and NCD) , with moIn this study, we combined canopy development modeling with an association mapping analysis to reveal the genetic basis of developmental, physiological, and agronomic traits with varying N availability. We applied established methodologies as used by D’hoop ,42. The Canopy cover of potato shows a very large genetic variation ,44,45,46Our results showed effects of N levels on the relationship between traits based on the genetic correlation , similart1) and a higher maximum cover (Vx) for a longer period (t2–t1) at high N input, all resulting in higher photosynthetic potential ) in the tubers, assessed using the Kjeldahl protocol.®, with percentage soil cover as the dependent variable of Beta thermal time counted from emergence day until each assessment date. Five parameters were estimated for each individual plot ) in g/kg (determined only in tubers); DM% in percentage; dry matter yield (Y_DM) in kg/m2, i.e., Y × DM%/100; N uptake in tuber (NUpt) in g/m2, i.e., Y_DM × [N]; N use efficiency (NUE) as Y_DM/(N input) in kg/g; N utilization efficiency (NUtE), i.e., Y_DM/NUpt, in kg/g; N uptake efficiency ; and soil coverage yield index (SCYi = AUC/Y_DM in %.td/(kg/m2)). The variables were analyzed without transformation since there were no severe violations to the assumptions required for mixed model analysis. All trait acronyms are summarized in On the basis of the parameters estimated with the CDv model, the following variables were calculated ,17,18: t∗” represent individual effects plus the interactions (yr∗ Mt = yr + Mt + yr.Mt), whereas terms joined by “.” represent interaction only. The term yr represents year, clarifying that year effects include variation due to the experimental field. The term Mt is the maturity group excluding control plot information. Corrections for rows and columns are the random terms (yr.row and yr.col). The term Mt.G represents the cultivars nested within maturity groups, since maturity is an intrinsic characteristic of each cultivar. Lastly, E represents the error. All random terms are underlined.Data were analyzed with the Genstat package (16th edition). The model in Equation (2) combining information of both years was used for each N level.The genetic correlations between traits were estimated as the Pearson correlations based on the estimated genotypic means, BLUEs, i.e., best linear unbiased estimates ). In addition, in order to understand relationships between traits and to define groups of traits, a divisive hierarchical cluster analysis was carried out, i.e., the analysis was top-down, performing recursive splits when going down the hierarchy. For this hierarchical cluster analysis, we used the absolute genetic correlations between traits as a similarity measure and applied the Ward minimum-variance method. The hierarchical cluster analysis was carried out separately for the two nitrogen levels. We excluded traits for which calculation included N input level, i.e., NUE, NutE, and NUptE. Additionally, biplots were generated to visualize relationships between traits per N level, included in The analysis included the 169 potato cultivars out of the total set of 189 cultivars for which genotypic data were available. SNP data were generated using a 20K Infinium SNP array . The dosThe GWAS was performed using a mixed model including a kinship matrix to account for population structure. The kinship matrix was estimated using 764 SNP markers randomly distributed over the genome, expressed as (with random terms being underlined):https://edepot.wur.nl/392278 (accessed on 20 July 2021).Linkage disequilibrium between markers was extensively studied by Vos et al.  and D’ho10(p) > 4. Then, the focus was to find marker–trait associations consistent across both years, for each N level. Next, we compared results from the two N levels defining associations detected at both N levels as common (cm), as well as those exclusive to either high N (HN) or low N (LN). The last two categories were considered N-dependent marker–trait associations.The association analysis was done using fitted values for the observations,  using the model of Equation (2). Four phenotypic datasets corresponding to combinations of both years and N levels were the input in this analysis. In Since maturity is known to have a strong effect on the traits considered in this study ,16,17,18


While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database ) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb.Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at The online version contains supplementary material available at 10.1186/s13073-021-00904-z. Genome-wide association studies (GWAS) have identified hundreds of thousands of genomic regions that are associated with complex human traits and have increased our understanding of the genetic architecture of human disease . While GComparative analyses of GWAS have revealed that numerous, seemingly unrelated traits are connected by shared underlying genetic variants . This phhttps://github.com/tbalmat/iCPAGdb [http://cpag.oit.duke.edu) to identify and explore shared SNPs between their own GWAS and a deep catalog of 4418 molecular, cellular, and disease phenotypes. Using a GWAS of severe COVID-19 [Motivated to simultaneously connect human phenotypes with shared genetic architecture and to identify the precise loci driving this similarity, we previously developed a method, CPAG , which estimated phenotype similarity of NHGRI-EBI GWAS catalog  traits b/iCPAGdb ), a new COVID-19  as the qhttps://www.ebi.ac.uk/gwas/) [http://h2p2.oit.duke.edu) [http://metabolomics.helmholtz-muenchen.de/gwas/) [p value threshold can be found in Table Publicly available GWAS summary statistics were downloaded from the following sources: 3793 traits from NHGRI-EBI GWAS Catalog (k/gwas/)  (versionuke.edu) , and 546e/gwas/) , 18. NHGhttps://grasp.nhlbi.nih.gov/Covid19GWASResults.aspx) [https://github.com/genomicsITER/PFgenetics.Severe COVID-19 summary statistics  generated by Ellinghaus et al.  were dowPLINK v1.9 [p value threshold of 5 × 10−8 (--clump-p1 5e-8). For molecular and cellular GWAS, we used a varying p value cutoff from 1 × 10−3 to 1 × 10−5 for --clump-p1 parameter to choose the index SNPs.GWAS summary statistics were individually pre-processed by LD clumping using INK v1.9 , based oINK v1.9 . The genp value for --clump-p1 to select the index SNPs and choose proper LD structure  based on the ancestry of the GWAS.For uploaded GWAS data, iCPAGdb calls on PLINK automatically to perform LD clumping. Users can define the R2 values were computed for each leading SNP against its surrounding SNPs using the genotypes from the 1000 Genomes project (Phase 3 genotypes) [R2 of pairwise SNPs within 10,000 bp windows were then calculated, and only LD proxies with R2 > 0.4 were retained in further analysis. The PLINK parameters for calculating LD were “--ld-window-kb 1000 --ld-window 10000 --keep-allele-order --r2 in-phase with-freqs gz.” The choice of R2 threshold of 0.4 was a practical decision made to reduce multiply counting regions with broad association signals in moderate LD, while maintaining sensitivity and specificity in detecting cross-phenotype associations. This R2 threshold was implemented in V1.0 of the iCPAGdb software used for all analysis in this manuscript and implemented in the web portal described below. However, users can also download the iCPAGdb V1.1 source code and population-specific LD proxy databases from Github  or lower LD (R2 > 0.2). While performing colocalization analysis is a prudent subsequent step in pursuing any iCPAGdb results, we caution that this is especially true when using the R2 cutoff of 0.2, because the iCPAGdb analysis may be identifying two distinct association signals in the same region or a single shared signal being harder to detect due to noisy data.To maximize phenotypic associations due to indirect associations, pairwise LD notypes) . Prior t/iCPAGdb ) and runSince GWAS may be performed on diverse populations from different ancestry or continents, we calculated ancestry-specific LD proxies for European, African, and Asian populations separately. European population included 503 samples from 5 populations , African included 661 samples from 7 populations , and Asian population included 504 samples from 5 populations . We filtered genotypes for each ancestry population by minor allele frequency more than 0.01 and retained only biallelic SNPs. SNPs which have the same genome coordinates were merged using “--merge-equal-pos.” For duplicated SNPs with the same variant rsID, we kept only the first variant by using “--rm-dup force-first” using PLINK 2.0.R2 > 0.4, these SNPs were merged into a SNP block until no further LD was found across shared SNP/LD pairs. The similarity of traits pairs based on shared associated SNPs was quantified using the Chao-Sorensen and Jaccard similarity index as described [Cross-phenotype SNPs were used to quantify the similarity of different traits. Cross-phenotype loci were identified as leading SNPs and/or their LD proxies having statistically significant associations with more than one trait/disease. If two traits shared a common leading SNP, we termed this “direct association.” If a leading SNP was associated with one trait, while its LD proxy SNPs were associated with another trait, we called this “indirect association.” If any shared SNP was in LD with another SNP with escribed . In thisNe is the effective number of independent SNPs in the selected population, n1 and n2 are the number of independent SNPs associated with trait 1 and trait 2, and k is the number of independent SNP blocks. The effective number of independent SNPs for European, African and Asian population was obtained from Table 4 from [where e 4 from .The significance of associations for all trait pairs was further corrected for all possible pairwise comparisons using the Benjamini-Hochberg and Bonferroni methods for multiple test correction. A false discovery rate of 0.1 was chosen to identify significantly correlated trait pairs.−8) were used by iCPAGdb. We quantified the strength of cross-phenotype similarity for each trait pair using the Chao-Sorensen similarity index. Since the p values from [p values for rg using the R “p.adjust” function with a total number of 276 comparisons.Bulik-Sullivan et al.  developeues from  were notcoloc.abf” function using the default prior parameters, p1 = 1 × 10−4, p2 = 1 × 10−4, and p12 = 1 × 10−5. We also ran the built-in “sensitivity” function to evaluate the robustness of predefined priors, and all tests suggested that default prior parameters were robust. Therefore, we ran all colocalization analyses with default priors.To evaluate whether the associations of GWAS trait pairs identified by iCPAGdb were due to sharing the same causal variants, we performed colocalization analysis using the R COLOC packages . COLOC uDPP9 expression in these data was carried out in R, and p values were calculated using the Wilcoxon rank-sum test.Transcriptomics of COVID-19 peripheral blood was generated by a previous study as described . For thahttps://github.com/tbalmat/iCPAGdb [p values. The ancestry-specific LD proxy tables contain pairwise SNPs’ rsID and R2 values (R2 > = 0.4) for different populations. All SQLite tables were indexed on unique combinations of SNP and trait or SNP pairs for LD proxy tables, which greatly reduces the searching time. To further increase calculation speed, the core cross-phenotype analysis part of iCPAGdb is parallelized by utilizing multiple threads.iCPAGdb is comprised of two core parts, the back-end computation and the front-end web browser. All code is available at /iCPAGdb . The bacp values), specifies format and column configuration, then uploads the file. Next, iCPAGdb computation parameter values, including iCPAGdb GWAS set to be crossed with, significance thresholds for filtering, and linkage disequilibrium (LD) population are specified. When “Compute CPAG" is pressed, the R script composes a system level command to execute the CPAG (Python) function. The future function of the R future package [Primary software components for the web portion of iCPAGdb are the R statistical programming language , the R p package  combinedp < 5 × 10−8) across 3793 traits from the NHGRI-EBI GWAS catalog, incorporates additional GWAS datasets  . As iCPAGdb quantifies overlapping SNPs to detect shared genetic associations rather than genetic correlation based on genome-wide summary statistics, iCPAGdb may be detecting instances where discrete loci are shared (perhaps suggesting shared biology or pleiotropy), even though the genome-wide genetic correlation is weak. Another possibility is due to the fact that iCPAGdb does not take into account directionality of effects for quantifying SNP overlap. Shared SNPs with inconsistent direction of effect between traits can lead to overall low genetic correlation by LD score regression, even when many SNPs are shared, as has been previously described for some autoimmune diseases [Results of iCPAGdb are consistent with results from the orthogonal approach of genetic correlation by LD score regression . Comparidiseases .In a previous study , we defip < 5 × 10−8, 13 loci overlap, including 9 loci identified only by LD proxy . These loci are spread across 7 chromosomes and include several solute carrier (SLC) and ATP-binding class (ABC) transporters that control urate absorption and secretion. Some of the loci are in close proximity but are counted separately by iCPAGdb, as could occur if different GWAS studies locate nearby peaks that fall below our R2 > 0.4 threshold or if multiple causal signals are located in the same region. These data provide genetic evidence for the well-known causal role of excess uric acid in gout and further reveal multiple genes that may serve as therapeutic targets. Inhibitors of renal uric acid reabsorption through URAT1 (SLC22A12) are commonly used in treating gout [p = 5.9 × 10−9). Finally, gout shares 2 loci (out of 5 from [p = 1.0 × 10−6), providing a human genetic rationale for the use of alpha-1-antitrypsin-based therapeutics in acute gouty flares  wit in mice ). Thus, −83) and Alzheimer’s disease (p = 8.9 × 10−17) as well as glucose with type II diabetes mellitus (p = 5.3 × 10−40). Other cross-phenotype associations highlight genetic variation that can extend our knowledge. For example, cross-phenotype associations were found between malaria [p = 1.4 × 10−9). This overlap is driven by well-known genetic variation in the beta-hemoglobin gene (HBB) and ABO blood type affecting malaria risk but also by genetic variation in ATP2B4 which encodes a calcium transporter. To the best of our knowledge, whether size of red blood cells impacts susceptibility to malaria parasites has not been examined. These cross-phenotype associations demonstrate the promise of this approach for revealing novel relationships that can be mined through iCPAGdb.Shared genetic associations reveal other well-known molecular and cellular disease relationships such as LDL cholesterol levels with cardiovascular disease , and 79 cellular host-pathogen interaction traits from our dataset of cellular host-pathogen interaction GWAS, H2P2 (http://h2p2.oit.duke.edu) [The above examples of clinically relevant cross-phenotype associations involving metabolite and cellular phenotypes motivated expansion of iCPAGdb to additional datasets. We used three datasets to provide molecular and cellular traits to our analysis: 491 metabolites and xenobiotics in blood  and 55 muke.edu) . iCPAGdbp = 3.9 × 10−7; 99,084-fold enrichment). iCPAGdb also revealed 7 other serum metabolites including glycine that shared an association with rs715 but not with the second MacTel locus. While serine was not part of the urine metabolomics dataset, iCPAGdb did detect overlap of glycine in urine and MacTel type 2 (p = 0.01).Cross-phenotype associations with macular telangiectasia (MacTel) type 2, a disease characterized by loss of central vision due to alterations in blood vessels in the macula of the eye, confirmed the importance of the amino acid serine  and risk of inflammatory bowel disease [p = 0.04). p values for the two associations are strongly correlated  exposed to 7 different pathogens . Notably disease . iCPAGdb disease  . A preprint recently confirmed this association across populations, and these authors speculated that ABO may affect CD209 levels to regulate SARS-CoV-2 entry [We applied iCPAGdb to a recently published GWAS of severe COVID-19 with respiratory failure . While tCOVID-19 , 78. Thery state . In factry state –82 and lry state –87. Furtry state . Most in-2 entry . Indeed,-2 entry , 91.FigABO regulates CD209 protein levels to impact COVID-19 risk, though much future experimental and clinical studies will be required to fully test this hypothesis  gene associated at p < 1 × 10−5 with severe COVID-19 was identified as being shared with a GWAS of fibrotic idiopathic interstitial pneumonia [p = 5.2 × 10−6 [p = 3.98 × 10−12 [R2 = 0.95 in 1000 Genomes European populations). Thus, iCPAGdb alerted us to the importance of a suggestive COVID-19 susceptibility locus that has since been validated in an independent cohort.Beyond neumonia  and a re2 × 10−6 ;). Much 2 × 10−6 , 94. Whi2 × 10−6 , this SN × 10−12 ;; R2 = 0DPP9 (and no other genes in the region) in GTEx , with onavirus . Additioonavirus –100. ColDPP9 in COVID-19, we analyzed previously generated transcriptomics of peripheral blood from COVID-19 patients [DPP9 expression across 46 COVID-19 patients were compared to individuals with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. DPP9 levels were significantly increased in COVID-19 patients compared to the other groups . Comparing COVID-19 data vs. each comparator individually revealed that DPP9 levels were elevated vs. healthy controls (p = 0.0016) and bacterial infection (p = 0.0078) but not influenza or other coronavirus infection , symptom severity dramatically improved over this period . We speculate that DPP9 may be induced to effectively turn off the inflammatory response to SARS-CoV-2 to minimize tissue damage and fibrosis. Combined with our human genetic data, these findings suggest that insufficient induction of DPP9 expression could predispose to severe COVID-19.To further examine the role of patients . Levels As the above examples demonstrate, iCPAGdb analysis can rapidly generate hypotheses connecting molecular and cellular traits to human disease. The website allows quick access to the pre-calculated cross-phenotype associations results described in this manuscript. Users can also upload their own GWAS summary statistics for comparing against all 4418 GWAS traits in the iCPAGdb website, facilitating the discovery of new cross-phenotype relationships. Total time for uploading, clumping of summary statistics, and calculation of cross-phenotype associations is typically < 2 min.The expansion of GWAS studies to more molecular, cellular, and human disease traits requires the development and implementation of new tools to facilitate drawing meaningful connections between phenotypes and understanding the molecular mechanisms that explain this shared genetic architecture. Our work demonstrates that leveraging available GWAS summary statistics and efficient algorithms of integrating pleiotropic information using ancestry-specific LD structure can rapidly reveal cross-phenotype associations across different phenotypic scales, which can be applied in real-time to better understand ongoing health crises such as the SARS-CoV2 pandemic.In examining cross-phenotype connections, it is important to carefully examine the overlapping SNPs provided as part of the iCPAGdb output to determine (1) the genome location where the variants are located, as some may be adjacent/overlapping loci in weak LD and not truly distinct and (2) how well identified GWAS signals from two traits overlap. Indeed, we view iCPAGdb as the first step in a pipeline for gaining greater understanding of any GWAS that then moves to colocalization analysis  and glycosylation has been observed by mass spectrometry (http://glycositeatlas.biomarkercenter.org/glycosites/33001/) [While our work highlights shared genetic architecture regulating n of ABO –82. For /33001/) . Whether/33001/) –104, 15 /33001/) . HoweverDPP9 may have a common role in pathogenesis in these diseases. iCPAGdb was able to identify this connection in the first published COVID19 GWAS despite the DPP9 allele being below genome-wide significance in that cohort, demonstrating the utility of iCPAGdb in expanding the power of GWAS studies on emerging and understudied diseases. We speculate that characteristics of inflammasome-mediated responses, normally suppressed by high expression of DPP9, may predispose to fibrosis. The shared genetic architecture also suggests that therapeutic approaches targeting fibrosis may be beneficial in both conditions. Pirfenidone and Nintedanib are anti-fibrotic FDA-approved drugs used to treat IPF, and our findings support the idea that these drugs may prove beneficial in COVID-19 [The shared underlying genetic risk factors for IPF and COVID-19 suggest that COVID-19 –108.As our examination of COVID-19 demonstrates, iCPAGdb is a powerful hypothesis engine that will lead to a deeper understanding of the genetic underpinnings of human disease risk, severity, and drug response.Project name: iCPAGdbhttp://cpag.oit.duke.edu and https://github.com/tbalmat/iCPAGdb [Project home page: /iCPAGdb Operating system(s): Red Hat Enterprise Linux OS, v7.9Programming language: Python v3.6 and R Shiny v1.5Other requirements: PLINK v1.9; R packages: shiny, shinyjs, DT, RSQLite, ggplot2, heatmaply, plotly, future; Python packages: panda, scipy, joblib, tqdm, sqliteLicense: GPL v3Any restrictions to use by non-academics: No restriction for non-academic useAdditional file 1. iCPAGdb Web App Supplement.Additional file 2: Figure S1. Clumping of GWAS results from NHGRI-EBI GWAS catalog.Additional file 3: Table S1. iCPAGdb output for cross-phenotype associations from NHGRI-EBI GWAS catalog .Additional file 4: Figure S2. Histogram of shared SNPs for each trait pair in NHGRI-EBI GWAS catalog from iCPAGdb at false discovery rate of 0.1.Additional file 5: Figure S3. Comparison of CPAG1 and iCPAGdb using the NHGRI-EBI GWAS catalog summary statistics downloaded on September 4, 2013.Additional file 6: Table S2. iCPAGdb output for cross-phenotype associations between molecular and cellular datasets and NHGRI-EBI GWAS catalog.Additional file 7: Table S3. COLOC analysis of CXCL10 following C. trachomatis infection and levels of CXCL9 (MIG) in whole blood.Additional file 8: Table S4. Cross-phenotype associations for COVID-19  against 4418 GWAS traits in iCPAGdb .Additional file 9: Table S5. COLOC analysis output for ABO region between COVID-19 and CD209 antigen levels.Additional file 10: Table S6. COLOC analysis output for DPP9 region between COVID-19 and idiopathic pulmonary fibrosis.