To what extent are shared genetic determinants in the comorbidities and associations between gastrointestinal tract diseases and psychiatry disorders involved in the gut-brain axis?In this genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources, pervasive genetic correlations and genetic overlaps between gastrointestinal tract diseases and psychiatric disorders were found. The pleiotropic genetic determinants between them were extensively distributed across the genome.These findings not only support the shared genetic basis underlying the gut-brain axis but also have important implications for intervention and treatment targets of these 2 types of diseases simultaneously. This genome-wide pleiotropic association study sequentially investigates the pleiotropic associations from genetic and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases and 6 psychiatric disorders. Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear.To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways.This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation , and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases  and 6 psychiatric disorders . Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022.The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders.INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8).Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously. The GBA is characterized by bidirectional interactions between the gastrointestinal tract and the central nervous system (CNS) and would link intestinal dysfunction and inflammation with brain function and psychiatric disorders.5 Various biological mechanisms were involved in the GBA, such as inflammatory immune responses, the autonomic nervous system, and enteric nervous system, where the role of the composition of gut microbiota and related metabolites has been particularly highlighted.7 Underlying the conceptual framework of the GBA, the shared genetic etiology might be involved in the associations between gastrointestinal tract diseases and psychiatric disorders.The comorbidities and associations between gastrointestinal tract diseases and psychiatric disorders have been widely reported,10 Genetic correlations have been suggested between these 2 types of diseases using linkage disequilibrium (LD) score regression (LDSC).14 However, it remains unclear whether the overall genetic correlation would be attributed to a few loci or across the genome.15 Indeed, there would be genetic overlap even without any genetic correlation. Although previous studies have investigated genetic overlap,16 shared susceptibility genes,18 and causal relationships21 between these 2 types of diseases, they mainly focused on inflammatory bowel disease (IBD) and psychiatric disorders with limited sample sizes. Recently, 2 studies22 have conducted GWAS of specific gastrointestinal tract diseases as well as systematic post-GWAS analyses and pointed out the necessity to explore the shared genetic risk across traits to improve the understanding of the disordered brain-gut interactions. Therefore, it is of great importance to further seek out the specific genomic variants or loci accounting for genome-wide genetic correlation and to deeply probe into the shared genetic etiology between these 2 types of diseases. Shared genetic etiology also indicates the potential pleiotropy, which often acts as genetic confounding of the associations between trait pairs.25 Cross-trait analysis has been proposed to investigate the pleiotropic genetic variants or loci among multiple traits by leveraging the correlation of GWAS signals,29 where the pleiotropic loci could serve as intervention targets with the potential to simultaneously prevent or treat these diseases.Genome-wide association studies (GWAS) have identified multiple genetic variants  associated with gastrointestinal tract diseases and psychiatric disorders.In this genome-wide pleiotropic association study, using large-scale GWAS summary data, we performed a genome-wide pairwise trait pleiotropic analysis between 4 gastrointestinal tract diseases  and 6 psychiatric disorders  through various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide, SNV, and gene levels and biological pathways to disentangle the underlying shared genetic etiology. Of note, under the framework of pleiotropic analysis, we first performed the SNV-level analysis to detect pleiotropic variants and loci, followed by pairwise colocalization analysis to determine colocalized loci and gene-level analysis to identify candidate pleiotropic genes, based on which we further performed parallel phenotype and tissue-specific enrichment analysis to characterize the phenotype and tissue specificity as well as additional gene-level analysis to identify the tissue-specific and cell type-specific pleiotropic genes. We also highlighted the role of gut microbiomes in interpreting the shared genetic etiology, followed by mendelian randomization analysis to evaluate pairwise causal associations and partly characterize different types of pleiotropy .10 GWAS for IBS were obtained from a larger meta-analysis with 486 601 individuals (53 400 cases and 433 201 controls).22 GWAS for the 6 psychiatric disorders were from the Psychiatric Genomics Consortium, including schizophrenia,30 BIP,31 MDD,32 ADHD,33 PTSD,34 and AN.35 In addition, GWAS for early age-related macular degeneration (AMD)36 and cataract37 were obtained to serve as a common set of negative controls for both gastrointestinal tract diseases and psychiatric disorders, given these 2 disorders are relatively limited to the pathological lesions of intraocular contents, and previous studies also showed no significant genetic correlations between AMD and MDD38 as well as among cataract, GERD, and psychiatric symptoms.39 All GWAS were approved by relevant ethic committees, and written informed consent was obtained from all participants, with details provided in the eTable 1 in STREGA) reporting guideline.We sought GWAS summary statistics from publicly available data sources with European ancestry, owing to the limited availability of well-powered GWAS with other ancestries, and selected GWAS with sample sizes larger than 50 000 to ensure statistical power. GWAS for GERD, IBD, and PUD were obtained from the same gastrointestinal tract GWAS based on 456 327 individuals from UK Biobank (UKB).All analyses were performed after excluding SNVs in the major histocompatibility complex region (chromosome 6: 25-35 megabase [Mb]) due to its complex LD structure and restricted to biallelic SNVs with minor allele frequency larger than 0.01. Details of these methods are provided in 40 and high-definition likelihood41 were used to assess genome-wide genetic correlations for 24 pairwise traits. The intercept from LDSC could also indicate potential sample overlap between 2 GWAS. Given that genetic correlation only reflects the overall correlation across the genome, we further applied genetic analysis incorporating pleiotropy and annotation (GPA)29 to explore the overall genetic overlap between traits. The Bonferroni-corrected significant threshold was set at P < 2.08 × 10−3 (.05/24). In addition, negative control analysis was performed through LDSC between AMD and cataract and the 4 gastrointestinal tract diseases as well as the 6 psychiatric disorders, with Bonferroni-corrected significant threshold set at P < 2.5 × 10−3 (.05/20).Both LDSC26 Single-nucleotide variations with P < 5 × 10−8 for PLACO were considered significant pleiotropic variants. Functional mapping and annotation of genetic associations (FUMA)42 was applied to characterize potential pleiotropic loci, based on which a bayesian colocalization analysis43 was performed to further identify shared causal variants in each pleiotropic locus. We declared a colocalized locus with posterior probability of H4 (PP.H4) larger than 0.7.For the union set of pairwise traits with significant genetic correlation or genetic overlap, we used pleiotropic analysis under composite null hypothesis (PLACO) to identify potential pleiotropic SNVs.44 analysis on the genes located in or overlapped with the pleiotropic loci based on both PLACO outputs and single-trait GWAS to identify candidate pleiotropic genes, with the significance declared at the locus-specific Bonferroni-corrected 2-sided P < .05 for MAGMA analysis on PLACO results and 2-sided P < .05 for MAGMA analyses based on original single-trait GWAS. Further, we performed phenotype enrichment analysis based on the Mouse Genome Informatics platform45 to characterize the phenotype specificity of these pleiotropic genes against that of nonpleotropic genes by examining the differences in the proportions of genes associated with certain phenotypes in the pleiotropic gene group against that in the nonpleotropic gene group using the Fisher exact test. Then, we performed tissue-specific enrichment analyses to illustrate the tissue specificity of these pleiotropic genes using the deTS tissue-specific enrichment method46 based on 2 different reference panels, Genotype-Tissue Expression project (GTEx)47 and the Encyclopedia of DNA Elements project (ENCODE).48 We declared the significance with a nominal threshold (2-sided P < .05) for these 2 parallel enrichment analyses. In addition, we used E-MAGMA (expression quantitative trait loci [eQTL]–informed MAGMA)49 and transcriptome-wide association study analysis using joint-tissue imputation50 to further investigate the tissue-specific genes, parallelized with H-MAGMA (Hi-C–coupled MAGMA)51 to indicate the cell-type specificity, in which we declared the significance with locus-specific Bonferroni correction. Gene set enrichment analysis was also performed to identify potential biological pathways using the clusterProfiler package.52 Significantly enriched pathways were declared with normalized enrichment score greater than 2 and adjusted P < .05. Multitrait colocalization analysis with HyPrColoc 53 incorporating host-microbiome GWAS was performed to highlight the critical role of gut microbiome.Based on PLACO results, we further explored the shared biological mechanisms of these pleiotropic loci. We performed gene-level multimarker analysis of GenoMic annotation (MAGMA)54 as main analysis and in total 6 alternative mendelian randomization methods with different model assumptions as additional analyses for further validation. For main analysis, we chose a false discovery rate approach for multiple testing correction with the significance threshold being false discovery rate–adjusted P < .05, given that the commonly used Bonferroni correction is often too stringent for multiple nonindependent tests. The nominally significant threshold (P < .05) was used for alternative mendelian randomization methods. The Latent Heritable Confounder Mendelian Randomization (LHC-MR) method, which could account for sample overlap, was used for pairwise traits with potential sample overlap to further validate the mendelian randomization results.55Last, we conducted bidirectional mendelian randomization analysis for 24 pairwise traits between gastrointestinal tract diseases and psychiatric disorders to investigate the potential causal trait pairs , along with negative control analysis using AMD and cataract. We used the inverse-variance weighted method42 We characterized independent SNVs with r2 less than 0.6000 and lead SNVs with r2 less than 0.1000 within 1 Mb. Genomic risk loci were defined by merging genomic regions if the physical distance between lead SNVs was less than 250 kilobase.42For significant pleiotropic SNVs from PLACO, we applied FUMA to identify independent variants, characterize genomic risk loci, and annotate the functions of variants using LD information from the 1000 Genome Project phase 3 reference panel of European population.42 Single-nucleotide variations with P < 5 × 10−8 in each single-trait GWAS were also annotated by FUMA for comparison.Functional annotations, including ANNOVAR software tool categories (Bioinformatics), combined annotation-dependent depletion (CADD) scores, and RegulomeDB scores, were also provided by FUMA. Single-nucleotide variations with a CADD score larger than 12.37 were considered a potentially deleterious variant.Using genome-wide association summary statistics from publicly available data sources, pervasive significant genome-wide genetic correlations and genetic overlaps were found across 24 pairwise traits, among which 14 were identified from LDSC and 21 were identified from GPA . NotablyNCAM1 [OMIM 116930]) and 1q32.1 (mapped gene: INAVA [OMIM 618051]), suggesting the extensive pleiotropic effects of these loci. The top SNVs in 83 FUMA-annotated pleiotropic loci showed mixed directions of allelic associations  were identified as potential pleiotropic variants by PLACO in 19 trait pairs  was a significant eQTL for EP300 (OMIM 602700) encoding p300 protein, which plays an important role in cell proliferation and differentiation. The index SNVs rs681343 (PLACO P = 1.36 × 10−12 for PUD-schizophrenia) and rs601338 (PLACO P = 5.07 × 10−11 for PUD-ADHD) at 22q13.2 were in high LD (r2 = 0.996) and had almost identical eQTL regulation information (eTable 7 in FUT2 (OMIM 182100). The index SNV rs13107325 at 4q24 locus  was a significant eQTL for SLC39A8 (OMIM 608732), which encodes ZIP8 metal cation transporter. In addition, 7 index SNVs (6 unique) were identified with CADD scores larger than 12.37, in which 2 mRNA exonic variants had higher CADD scores, including rs601338  and rs13107325 .ANNOVAR category annotation illustrated that 34 of 83 index SNVs (41%) were intronic variants and 27 of 83 (33%) were intergenic variants. Only 7 of 83 index SNVs (8%) (6 unique) were exonic variants, including 5 messenger RNA (mRNA) exonic variants and 2 noncoding RNA exonic variants (eTable 5 in FUT2). In addition, 7 pleiotropic loci were identified with PP.H3 larger than 0.7000, indicating there might be different causal variants in these loci  with PP.H4 larger than 0.7, in which 22 top SNVs of corresponding loci were identified as candidate shared causal variants . InteresNCAM1 was identified as a significant pleiotropic gene in 5 pairs of traits, followed by INAVA, CACNA1S (OMIM 114208), and KIF21B (OMIM 608322) in 4 pairs of traits and BANK1 (OMIM 610292), CAMKV (OMIM 614993), DPYD (OMIM 612779), MON1A (OMIM 611464), MST1R (OMIM 600168), PSCA (OMIM 602470), RBM5 (OMIM 606884), RBM6 (OMIM 606886), and SLC39A8 in 3 pairs of traits.MAGMA analysis based on 295 potential pleiotropic genes that located in or overlapped with 83 pleiotropic loci identified 196 significant pleiotropic genes (158 unique), in which 38 genes were detected in 2 or more trait pairs (eTable 9 in P = 1.80 × 10−2)  B. Detail × 10−2)  in sever × 10−2) .PSCA (8q24.3) for 3 pairwise traits , LPR8 (1p32.3) for 2 pairwise traits (IBS-schizophrenia and IBS-BIP), and FUT2 (19q13.33) for 2 pairwise traits (PUD-schizophrenia and PUD-ADHD). H-MAGMA analysis further suggested the cell-type specificity of these pleiotropic genes. Details of these gene-level analyses were also provided  across 8 pleiotropic loci were significantly detected in multiple GBA-related tissues in at least 2 trait pairs, for example, P < .05), including 99 Gene Ontology (GO) terms and 28 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (eTable 16 in H1) and TH2 cell differentiation (KEGG has04658) and TH17 cell differentiation (KEGG has04659), which involve in immunoinflammatory responses, were highlighted in 2 pairs of traits.Gene set enrichment analysis identified 127 significantly enriched pathways  was also identified as shared causal variant.Multitrait colocalization analysis from HyPrColoc highlighted 15 pleiotropic loci (11 unique) that were colocalized to share a causal variant, supporting the significant role of gut microorganisms . Seven oBidirectional mendelian randomization analyses using inverse-variance weighted method showed 11 significant positive associations , 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). Several loci previously identified to be associated with gastrointestinal tract diseases were illustrated to be potential pleiotropic loci shared with psychiatric disorders and vice versa (eDiscussion in Overall, pleiotropic variants between gastrointestinal tract diseases and psychiatric disorders were extensively distributed, with several loci especially highlighted between certain trait pairs, such as 1q32.1 (H1, TH2, and TH17 cell differentiation pathways were highlighted between IBD and schizophrenia, BIP, and AN. The TH17 cells play important roles in the development of inflammatory responses and autoimmune diseases.58 Previous studies suggested the significant role of dysfunction of TH17 cell differentiation and the accumulation of TH17-related cytokines in the pathological process of IBD.60 In addition, TH17 cells and related cytokines would provoke CNS neuroinflammation and neurotoxicity under pathological status and disrupt the blood-brain barrier and thus alter the permeability of the blood-brain barrier.62 Bacterially produced bile acid metabolites have been shown to inhibit TH17 cell differentiation, which may be related to the pathophysiology of IBD.63 Therefore, TH17 cell differentiation and function play a vital role in the regulation of brain-gut-microbiome axis.Shared genetic determinants also reflect common biological pathways, among which the TOur study is not without limitations. First, we were unable to assess the causal effects of gut microbiome on gastrointestinal tract diseases or psychiatric disorders through mendelian randomization analyses since SNVs with associated gut microorganisms were hard to obtain due to the limited sample size of gut microbiome GWAS. Therefore, caution should be used on the interpretation of the role of gut microorganisms. Second, our study was restricted to European ancestry and may not generalize to other ancestries.Given strong evidence of genetic correlation and genetic overlap between gastrointestinal tract diseases and psychiatric disorders, we found that pleiotropic genetic variants, loci, and genes were extensively distributed across the genome, with higher phenotype and tissue specificity. We highlighted some genetic determinants previously associated with gastrointestinal tract diseases that were shared with psychiatric disorders and vice versa. More importantly, shared biological mechanisms concerning immune response, synaptic structure and function, and potential gut microbiome were identified. Our findings not only support the shared genetic basis underlying GBA but provide novel insight into the intervention and treatment targets of these diseases.


GDF15, potentially due to epitope-binding artefacts. After conditioning on rs1058587, statistical fine mapping identified four independent putative causal signals at the locus. Mendelian randomisation (MR) analysis found evidence of a causal relationship between GDF15 concentration and high-density lipoprotein (HDL) but not body mass index (BMI). Using reverse MR, we identified a potential causal association of BMI on GDF15 (IVW pFDR = 0.0040). Taken together, our data derived from human population cohorts do not support a role for moderately elevated GDF15 concentrations as a causal factor in human cardiometabolic disease but support its role as a biomarker of metabolic stress.Growth differentiation factor-15 (GDF15) is a stress response cytokine that is elevated in several cardiometabolic diseases and has attracted interest as a potential therapeutic target. To further explore the association of GDF15 with human disease, we conducted a broad study into the phenotypic and genetic correlates of GDF15 concentration in up to 14,099 individuals. Assessment of 772 traits across 6610 participants in FINRISK identified associations of GDF15 concentration with a range of phenotypes including all-cause mortality, cardiometabolic disease, respiratory diseases and psychiatric disorders, as well as inflammatory markers. A meta-analysis of genome-wide association studies (GWAS) of GDF15 concentration across three different assay platforms  confirmed significant heterogeneity due to a common missense variant  in  Obesity accounts for an estimated 2.8 million deaths worldwide and 2.3% of global disability-adjusted life years . ObesityGDF15, a distant member of the transforming growth factor-β family , is upreCurrently, there is a lack of data supporting GDF15 as a potential therapeutic target in humans. However, observational studies have revealed strong positive correlations of GDF15 plasma levels with body mass index (BMI), insulin resistance, age, and mean arterial blood pressure  analysis.To assess systematically the relationship between GDF15 plasma levels and clinical phenotypes, we utilised a large Finnish biobank, FINRISK. The FINRISK cohort comprises a cross-sectional population survey carried out over a 40-year period in Finland. GDF15 plasma levels were available for 6610 participants from the 1997 recruitment cohort linked to 676 disease outcomes and were included in this analysis.The median GDF15 concentration (measured using an immunoluminometric assay [ILMA]) was 796 ng/L (interquartile range [IQR] = 664–986). First, we examined the association of baseline characteristics with GDF15 plasma levels . Age expFDR < 0.05), GDF15 was significantly associated with 80 disease endpoints , focusing on the phenotypes defined by the FinnGen consortium , as thesndpoints .–27) and mortality due to cardiac diseases . We also observed significant associations between GDF15 levels and type 2 diabetes . Further, GDF15 plasma levels were significantly associated with cardiovascular diseases , as well as subtype endpoints such as atherosclerosis, hypertension, peripheral artery (PAD) disease and stroke, and chronic kidney disease , COPD, psychiatric disorders, and malignant neoplasm of respiratory system and intrathoracic organs .–11). GDF15 plasma levels were also significantly higher in incident cases than in non-diabetic controls .Type 2 diabetics  had a 1.3-fold increase in median GDF15 levels compared to non-diabetic controls (median difference = 222 ng/L). Furthermore, GDF15 levels were significantly higher in prevalent diabetics  compared to both incident cases  and non-diabetic controls . GDF15 plasma levels were also higher in incident cases than healthy controls .Similarly, GDF15 levels were significantly higher in prevalent atherosclerosis and prevalent COPD cases  compared to respective incident cases  and healthy controls  also showed similar results for which plasma GDF15 levels seemed to predict the disease . Togethe–12), type 2 diabetes , and cardiovascular disease .Next, we carried out a Cox regression survival analysis on all-cause mortality, type 2 diabetes, and cardiovascular disease. During a 10-year follow-up period, 393 (6%) study subjects had died, 97 (2%) developed diabetes, and 438 (7%) developed cardiovascular disease. Models accounted for blood pressure medication, smoking, total cholesterol, HDL cholesterol, BMI, prevalent cardiovascular disease, and mean systolic blood pressure. Our results revealed that GDF15 plasma concentration is an independent predictor of all-cause mortality , significant associations were observed with 45 biomarkers, most notably, with blood markers known to be associated with inflammation , C-reactive protein  and hepatocyte growth factor . All three markers have been previously reported to be non-specifically elevated in a number of human diseases and have known prognostic value (–22), fasting insulin  and waist-to-hip ratio (WHR) .The most significantly associated biomarkers were mid-regional pro-adrenomedullin . In INTERVAL we assayed plasma GDF15 in 3301 participants using SomaScan (an aptamer-based multiplex protein assays) and in 4998 participants using a proximity extension-based antibody assay (Olink). The most significant associations were found around the GDF15 gene on chromosome 19, comprising 134 significant variants for INTERVAL-SomaScan and 72 significant variants for INTERVAL-Olink  and three GDF15 quantification methodologies (ILMA in FINRISK and Olink and SomaScan proteomic assays in INTERVAL). Results from individual GWAS analyses are reported in GDF15 locus to be associated with GDF15 plasma levels  at the 2=99.8%, heterogeneity p-value = 7.6 × 10–180; Two variants had a strengthened signal in the combined meta-analysis, rs1054221 and rs1227734 , HoweverTo remove any potential epitope effects, we applied conditional analysis on rs1058587 when linking plasma GDF15 concentrations with disease phenotypes and quantitative traits. A total of 59 significant disease associations and 43 biomarker associations were identified. These associations largely did not differ from the unconditioned results, although 21 fewer disease associations reached significance .–187).To minimise confounding of the GWAS results from rs1058587 epitope effects, we conditioned the association signal on chromosome 19 on rs1058587 in both FINRISK  and INTE–184), rs3787023 , rs138515339 , and rs141542836  and had a regional heritability of 7%. Functional annotation can be found in Fine-mapping this conditioned meta-analysis revealed four association signals with a causality probability of 0.86. The top configuration consisted of variants rs1054221  and identified five independent genetic instruments from the conditioned meta-analysis on chromosome 19: rs111527728, rs113700483, rs1227734, rs150286074, and rs73923175  and WHR . The effects sizes for both of these were very small and the WHR result was not robust to multiple-testing correction in MR-PRESSO (We applied two-sample MR  using LDR-PRESSO . HorizonR-PRESSO .FDR = 0.0040) but not any other tested trait (We applied reverse two-sample MR (as described above) using GDF15 plasma levels as the outcome variable to assess associations with BMI, WHR, glucose, diabetes, HDL cholesterol, and eBMD as the MR exposures. GDF15 GWAS summary statistics were taken from the conditioned meta-analysis of GDF15 levels in FINRISK and INTERVAL in all chromosomes. LD clumping was used to identify the genetic instruments  of GDF15 on human disease. We analysed whole-exome sequencing data from 302,388 participants from the UK Biobank, of whom 109 carried GDF15 PTVs in the heterozygous state. We assessed differences in BMI, WHR, glucose, eBMD, and HDL cholesterol between carriers and non-carriers using the Mann-Whitney U test. We were not able to carry out this analysis on type 2 diabetes given too few patients in this subset (n=2). The analysis was restricted to unrelated individuals of European ancestry, leaving 91 carriers of GDF15 PTVs  and human diseases , as well as several inflammatory biomarkers. As preclinical data in mice strongly implicated Gdf15 in the aetiology of obesity and glucose tolerance, we specifically investigated whether human genetic evidence supports these findings. Using data from large biobanks, we found that neither MR nor Access to the FINRISK cohort provided a valuable opportunity to explore GDF15 plasma level associations with multiple phenotypes within a single large population. We found the strongest associations with all-cause mortality and cardiometabolic diseases (cardiovascular disease and diabetes), as well as cardiometabolic risk factors , as previously reported . Our anaTo define the genetic architecture of plasma GDF15 levels, we performed a GWAS meta-analysis in two large cohorts, FINRISK and INTERVAL, across three different assay platforms. A striking finding of this analysis was the substantial heterogeneity between variants across these studies, consistent with previous reports . ExploriGDF15 PTV carriers in the UK Biobank dataset, albeit only heterozygous LOF carriers could be assessed. A meta-analysis completed by Cheung et al. (2 ≤ 0.02) found no significant causal effect of GDF15 levels on BMI or diabetes traits  and physiological measures  were collected from the study participants. In addition, participants who consented were matched to their electronic health records giving access to hospital discharge registry (years 1969–2015), hospital discharge registry of specialist health care operations (1996–2015), death registry (1992–2015), drug reimbursement registry (1964–2015), drug purchase registry (1995–2015), and cancer registry (1953–2014). Gender was obtained from social security numbers. A group of clinicians working in the FinnGen consortium constructed 676 disease endpoints combining information from multiple health registries. ICD8, ICD9, and ICD10 codes were utilised in the disease endpoint definitions, for more information see The INTERVAL study is a prospective cohort study comprising approximately 50,000 participants nested within a pragmatic randomised trial of blood donors . BetweenInformed consent was obtained from all participants and the INTERVAL study was approved by the National Research Ethics Service (11/EE/0538).For the SomaScan assays, two non-overlapping subcohorts of 2731 and 831 participants were randomly selected, of which 3301 participants (2481 and 820 in the two subcohorts) remained after genetic quality control.For the Olink assay, protein measurements were conducted in an additional subcohort of 4998 INTERVAL participants aged over 50 at baseline; 4987 samples passed quality control for this panel and were included in the analyses.For information on genotyping in the INTERVAl cohort, see Suplementary materials.UK Biobank is a population-based cohort consisting of ~500,000 individuals with participants recruited between 2006 and 2010 and aged between 40 and 69. Recruitment and cohort information has been previously described . Electro2=0.99, p<0.001, n=31 samples: H). Head-to-head comparison of IRMA with the clinical ‘gold standard’ Roche assay have been previously conducted and reported GDF15 levels were comparable  to bind each protein target, and then uses proximity extension followed by qPCR to quantify protein abundance. Aliquots of plasma from samples taken from INTERVAL participants at the 2-year follow-up survey were shipped on dry ice to Olink Proteomics  for assay. Protein concentrations were recorded as normalised relative protein abundances (‘NPX’).-6), minor allele count (MAC) < 3  or MAC < 10  steps, the samples were pre-phased using Eagle2 (version 2.3). Genotype imputation was carried out by using a Finnish population-specific reference panel consisting of 2690 high-coverage WGS and 5092 WES samples with IMPUTE2 . The present study, using samples taken in FINRISK in 1997, consisted of 6538 individuals which were genotyped using three genotyping arrays: the HumanCoreExome BeadChip, the Human610-Quad BeadChip, and the Infinium HumanOmniExpress . Genotype calls were generated together with other available datasets using zCall at the Institute for Molecular Medicine Finland (FIMM). After sample-wise QC  and variant-wise QC (exclude SNPs with high missingness [>2%]), low HWE p-value (<1 x 10-6) or had a within-batch call rate < 0.97. Sample QC included removing duplicate samples and samples with non-European ancestry, missing phenotypic sex and sex mismatches, and extreme heterozygosity (±3 SD). Relatedness was removed by excluding one participant from each pair of close (first- or second-degree) relatives, defined as π > 0.187.The genotyping protocol and QC for the INTERVAL samples have previously been described in detail . DNA was-6, a call rate filter of 99% over the INTERVAL genotyping batches that a variant was not failed in, and a global call rate filter of 75% across all INTERVAL genotyping batches. All monomorphic variants, non-autosomal and multi-allelic variants were removed and 654,966 high-quality variants remained to be used for imputation. Phasing was conducted using SHAPEIT3 and variants were imputed using a combined 1000 Genomes Phase 3-UK10K imputation panel. Imputation was performed on the Sanger Imputation Server (https://imputation.sanger.ac.uk), resulting in 87,696,888 imputed variants.Additional variant QC steps were performed prior to imputation to establish a high-quality imputation scaffold. This included imposing a global HWE filter of p-value < 5 x 10For the FINRISK cohort genome-wide association analyses were performed for 5817 individuals with plasma GDF15 concentrations available. A normal distribution of GDF15 plasma concentrations was achieved through applying an inverse normal transformation. Multi-dimensional scaling was done for genetic data of both studies using PLINK version 1.07 . Only goThe GWAS for INTERVAL-SomaScan has previously been described in detail . RelativFor the INTERVAL-Olink GWAS, normalised protein levels (‘NPX’) were first regressed on age, sex, plate, time from blood draw to processing (in days), and season . The residuals from this linear regression were rank-inverse normalised. The rank-inverse normalised residuals were then fitted in a linear regression model adjusted for ancestry by including the first three components of multi-dimensional scaling as covariates. GWAS was conducted using SNPTEST version 2.5.2.2 value, and 1, 2, and 3 represent the three different cohorts: FINRISK, INTERVAL-SomaScan, and INTERVAL-Olink.Fine mapping was performed using the FINEMAP program . FINEMAPGTEx, Ensmbl, SNiPA, RegulomeDB  age and sex, (2) age, sex, and BMI, (3) age, sex, and smoking-adjusted models. Analyses using linear and logistic regression models were performed using R2.11 (see URLs). FDR was applied to test for multiple test correction.All analyses were performed in R version 3.4.0. Cox proportional hazard regression model was conducted to identify predictors of outcomes during 10 years. The survival curves for a Cox proportional hazards model were used to illustrate the timing of the death, type 2 diabetes, and CVD events during 10 years in relation to GDF15 quartiles, and statistical assessment between upper quartile and other quartiles was preformed using Cox proportional hazard regression model. To inspect the validity of the Cox model, the test of the proportional hazards assumption for a Cox regression model was used. Hosmer-Lemeshow goodness of fit test was used to determine whether models were calibrated with similar expected and observed event rates in both low- and high-risk individuals.Wilcoxon rank sum test (Mann-Whitney U test) was performed, in R using function ‘wilcox.test’ from stats package, to test whether the distributions of the incident disease case group, prevalent disease case group, and control group were systematically different from one another. Wilcoxon rank sum test was used for the prevalent and incident analysis given non-transformed GDF15 plasma levels  were used.The ICD codes used for type 2 diabetes endpoint definition in the incident prevalent analysis were: ICD9 codes: 2502A, 2501A, 2503A, 2504A, 2505A, 2506A, 2507A, 2508A, 2500A ; ICD10 codes: E110, E111, E112, E113, E114, E115, E11[6-8], E119 (adult type diabetes); ATC-code A10B  from kela drug purchase register as well as ICD10 code E11 (adult type diabetes) from kela reimbursement register. During the ICD8 era, type 1 and type 2 diabetes were not separated in Finnish ICD8 diagnosis codes, and thus ICD8 diabetes diagnosis codes were not included to the type 2 diabetes endpoint. To ensure that type 1 diabetes cases are not mixed with type 2 diabetes cases, type 1 diabetes endpoint cases were removed from the type 2 diabetes endpoint cases.In brief, MR is a method that explores whether an intermediate trait has a causal relationship with an endpoint by using genetic instruments. The use of genetic data means there is less environmental bias. Variants identified as significantly associated with the intermediate trait are applied and summary results from intermediate trait – variants and endpoint – variants are applied in the model. MR-Egger regression does not constrain the intercept, therefore it is not biased by invalid IV but has reduced power. MR relies on three main assumptions that (1) the instruments are associated with the exposure, (2) the instruments are not associated with any confounders, and (3) the instruments are associated with the outcome only through the exposure. Here, we applied random effect IVW analysis , MR-Egger , weighted median MR , and MR-PRESSO . The strengths and weaknesses listed here are summarised in this paper .http://www.nealelab.is/uk-biobank), type 2 diabetes , and eBMD  using the package ‘MendelianRandomization’. The IVW method from this package was applied to test for causality and a sensitivity analysis using MR-Egger to testing for horizontal pleiotropy. A finding of significant causality is indicated with the IVW p-value and a significant MR-Egger intercept p-value indicates a finding of horizontal pleiotropy. Weighted median MR and MR-PRESSO  at Regeneron Pharmaceuticals using the IDT xGen version 1 capture kit and NovaSeq6000 for more information, see previous publication . Data waGDF15 PTVs were identified by extracting variants annotated as PTVs . GDF15 PTV variants 20,000 males and 20,000 females were randomly extracted as controls.Individuals with GDF15 PTV carriers and non-carriers.Analysis was completed in R (version 3.2.4) using ‘wilcox.test’ and models compared BMI, WHR , diabetic status (self-reported), eBMD, and HDL in  This integrated observational and genetic analysis using data from large biobanks comprehensively investigated the role of Growth Differentiation Factor-15 in a wide range of human diseases and will be of interest to cardiometabolic disorder researchers. GDF-15 appears to be a marker of metabolic stress rather than having a causative role. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Edward D Janus as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Matthias Barton as the Senior Editor.Thank you for submitting your article "Integrated Analyses of Growth Differentiation Factor-15 Concentration and Cardiometabolic Diseases in Humans" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) There is room for improvement concerning Mendelian randomization. Whilst the use of Mendelian randomization design may circumvent issues with confounding in observational studies, there was a lack of certain analyses which may improve the robustness of findings, as well as concerns over the instruments which appeared to be correlated.The study is in line with a previous Mendelian randomisation study exploring the relation of GDF-15 in type 2 diabetes (https://pubmed.ncbi.nlm.nih.gov/31161347/) although different from another MR study (https://pubmed.ncbi.nlm.nih.gov/33686698/). It would be useful to include these studies in the Introduction, and compare and contrast the findings in this paper with previous MRs in the Discussion.2) Please briefly explain how MRs may be less vulnerable to confounding and the underlying assumptions needed. Please also mention the assumptions of the IVW and sensitivity analyses. Please consider the use of multiplicative random effect model for IVW which is more robust to outlier influence. Would also be good to include weighted median method. R2 for LD should be more stringent (i.e. <0.01 or <0.001). Otherwise, the analyses used would be invalid given the use of correlated variants. The authors could also consider using analyses which take into account correlated variants. Please also provide more information in how the genetic instruments were identified . There is also no mention of instrument strength such as F statistics.3) Why were meta analyses only performed for SNPs around the GDF-15 gene instead of the entire sets of SNPs across different chromosomes? (Page 26)4) Given the authors mentioned metformin it would be worth discussing this paper (https://pubmed.ncbi.nlm.nih.gov/27974345/) although metformin use is associated with higher GDF-15.5) Please provide more details regarding which phenotypes were used to define type 2 diabetes. I presume these included the use of self reports and anti-diabetic medications?6) In the PTV analyses there were only 2 type 2 diabetes cases so is such analysis valid.7) Please provide further elaboration on the rationale of the Willcoxon test for prevalent and incident diabetes.8) Why was GDF-15 transformed? I think this is not necessary for using GDF-15 as a predictor in a regression and would complicate the interoperation of the unit change.9) For the association analysis of GDF15 plasma levels with disease outcomes, symmetrically grouping relevant disease outcomes may be more informative and this will eliminate the redundancy of the same subject being included in multiple outcomes.10) For the GDF15 plasma concentration associations with prevalent and incident cases, why was only diabetes was analyzed? At least pulmonary related diseases, as a whole, should be analyzed. This section is very informative.11) More analysis should be done for the GDF15 and biomarker associations instead of simple descriptive analysis.12) If BMI is the driving force for GDF15, how to explain that GDF15 has much stronger effects than BMI in the Cox proportional hazard analysis presented in Figure 1?13) More detailed analysis with BMI could help to close the loop of BMI-GDF15-mortality.14) The truncated variants had no significant effects but given the subjects also had a normal allele this presumably compensated for the truncated allele. A homozygote or compound heterozygote for a non functioning allele could be highly informative but will probably be rare and hard to find. This could be further discussed. Essential revisions:1) There is room for improvement concerning Mendelian randomization. Whilst the use of Mendelian randomization design may circumvent issues with confounding in observational studies, there was a lack of certain analyses which may improve the robustness of findings, as well as concerns over the instruments which appeared to be correlated.The study is in line with a previous Mendelian randomisation study exploring the relation of GDF-15 in type 2 diabetes (https://pubmed.ncbi.nlm.nih.gov/31161347/) although different from another MR study (https://pubmed.ncbi.nlm.nih.gov/33686698/). It would be useful to include these studies in the Introduction, and compare and contrast the findings in this paper with previous MRs in the Discussion.Thank you for drawing our attention to these studies. We have now included them in our manuscript in both the introduction:“Opposing results have also been reported regarding the association of GDF15 on BMI, with one study finding a causal relationship  and another reporting no significant effect ”.And discussion:2≤0.02) found no significant causal effect of GDF15 levels on BMI or diabetes traits . In contrast to these results, Karhunen et al., recently reported a significant causal association of GDF15 levels with BMI; the study used instruments that included SNPs in strong LD with rs1058587 the variant we identified to lead to a binding artefact in the assays , highlighting the importance of exploring assay bias in genetic studies”.“Similarly a further study using genetic instruments weakly correlated to rs1058587  Please briefly explain how MRs may be less vulnerable to confounding and the underlying assumptions needed. Please also mention the assumptions of the IVW and sensitivity analyses. Please consider the use of multiplicative random effect model for IVW which is more robust to outlier influence. Would also be good to include weighted median method. R2 for LD should be more stringent (i.e. <0.01 or <0.001). Otherwise, the analyses used would be invalid given the use of correlated variants. The authors could also consider using analyses which take into account correlated variants. Please also provide more information in how the genetic instruments were identified . There is also no mention of instrument strength such as F statistics.Thank you for your suggestions. We have now implemented a random-effect IVW analysis and reduced the R2 threshold to < 0.01 (R2 < 0.001 resulted in too few genetic instruments). The updated results can be found in Tables 3 and 4 and in the text on pages 13-16. Briefly, the horizontal pleiotropy is reduced across all MR analyses and we additionally find significant associations with HDL as well as WHR . We have added the results for the weighted median analysis in the supplementary materials .-8.We have also added more information on the assumptions of the methods applied as well as the description of how the genetic instruments were identified and added the F-statistics. To ensure strong instruments were applied, we set a p-value threshold of 5x10The assumptions of the MR techniques are discussed in the methods section and read:“Mendelian randomisation relies on three main assumptions (1) that the instruments are associated with the exposure, (2) the instruments are not associated with any confounders and (3) the instruments are associated with the outcome only through the exposure. Here we applied random-effect IVW analysis , MR-Egger , Weighted Median MR  and MR-PRESSO . The strengths and weaknesses listed here are summarised in this paper”.2<0.01, p-value < 5x10-8 and clump radius < 3000kb”.We have added the following to the manuscript to provide further information on how the instruments were identified: “LD clumping was carried out in PLINK (version 1.9) with the following thresholds applied: RF-statistics are reported for all exposure SNPs in Supplementary File 4, Tables S4a and S4c-g.3) Why were meta analyses only performed for SNPs around the GDF-15 gene instead of the entire sets of SNPs across different chromosomes? (Page 26)We have updated the Results section to read: “We then performed a meta-analysis across the genome for all three cohorts. Only signals within 1Mb of the GDF15 gene reached significance in the meta-anlaysis.”. Additionally we have updated Figure 2 to include the full GWAS Manhattan plot.4) Given the authors mentioned metformin it would be worth discussing this paper (https://pubmed.ncbi.nlm.nih.gov/27974345/) although metformin use is associated with higher GDF-15.Thank you for drawing our attention to this interesting paper. We have commented on this in the introduction to expand our discussion on the relationship between GDF15 with Metformin. It now reads: “In fact, an analysis of changes induced by Metformin treatment in 237 blood biomarkers showed GDF15 levels to be the most significantly altered, independent of glucose and glycosylated haemoglobin”.5) Please provide more details regarding which phenotypes were used to define type 2 diabetes. I presume these included the use of self reports and anti-diabetic medications?We thank the reviewer for noticing this point. The FINRISK analyzes in the manuscript were performed during the FinnGen piloting project. Both ICD9/10 codes from the Hilmo inpatient, outpatient registers and cause of death registers; ATC-codes from KELA drug purchase register; and ICD10 codes from KELA drug reimbursement register were used for defining these endpoints. The endpoint used in T2D prevalent/incident analysis is E4_DM2 (E4 Type 2 diabetes), from which E4_DM1 (E4 Type 1 diabetes) endpoint cases have been removed to ensure that there are no type 1 diabetes cases in the T2D case group. More details can be found here https://www.finngen.fi/en/researchers/clinical-endpoints. These disease endpoints have been reviewed by clinical groups within FinnGen.We have now included a full definition in the methods section:“The ICD-codes used for Type 2 diabetes (T2D) endpoint definition in the incident prevalent analysis were: ICD9 codes: 2502A, 2501A, 2503A, 2504A, 2505A, 2506A, 2507A, 2508A, 2500A ; ICD10 codes: E110, E111, E112, E113, E114, E115, E11[6-8], E119 (adult type diabetes); ATC-code A10B  from KELA drug purchase register as well as ICD10 code E11 (adult type diabetes) from KELA reimbursement register. During the ICD8 era, type1 and type2 diabetes were not separated in Finnish ICD8 diagnosis codes, and thus ICD8 diabetes diagnosis codes were not included to the T2D endpoint. To ensure that Type 1 diabetes cases are not mixed with Type 2 diabetes cases, T1D endpoint cases were removed from the T2D endpoint cases.”6) In the PTV analyses there were only 2 type 2 diabetes cases so is such analysis valid.Thank you for raising this point. We have removed this analysis and rephrased this within the text to now read:“We were not able to carry out this analysis on type 2 diabetes given too few patients in this subset (n=2)”.7) Please provide further elaboration on the rationale of the Willcoxon test for prevalent and incident diabetes.We thank the reviewers fot this suggestion and have expanded the rationale on the use of the Wilcoxon test below.Wilcoxon rank-sum test is a nonparametric test used to compare two independent samples, while Wilcoxon signed-rank test is used to compare two dependent samples. The Wilcoxon rank-sum test is commonly used for the comparison of two groups of nonparametric, not normally distributed, data. Non-parametric tests have two advantages over parametric tests: they do not require the assumption of normality of distributions and they can deal with outliers. A Student’s t-test for instance is only applicable if the data are Gaussian or if the sample size is large enough . A non-parametric test should be used in other cases.In prevalent-incident T2D (and other endpoints) analysis non-transformed GDF15 values were used, which were not normally distributed. In addition, the sample size of prevalent T2D was only 37. Thus we decided to use Wilcoxon rank sum test in T2D prevalent/incident analyses.:We have now specified the test used and additionally have added the sentence“Wilcoxon rank sum test was used for the prevalent and incident analysis given non-transformed GDF15 plasma levels  were used.”8) Why was GDF-15 transformed? I think this is not necessary for using GDF-15 as a predictor in a regression and would complicate the interoperation of the unit change.We thank the reviewer for raising this point. The GDF15 distribution was not normally distributed and had large tails/outliers. Given these outliers may represent potential participants of interest, we took the decision not to remove them and inverse normalise the data. Whilst we understand this is not always necessary for a dependent variable in regression, we wanted to eliminate any potential bias that could be introduced by skewed data.9) For the association analysis of GDF15 plasma levels with disease outcomes, symmetrically grouping relevant disease outcomes may be more informative and this will eliminate the redundancy of the same subject being included in multiple outcomes.Thank you for this suggestion. We have tried to be as inclusive as we can in our analysis and have therefore considered as many phenotypes as possible, but we acknowledge that there may be redundancy. The argument for ‘clumping’ or ‘splitting’ is an ongoing discussion that has not yet come to a definitive conclusion (e.g. see https://www.nature.com/articles/s41593-020-0609-7); here, we have included both the parent groups and the subgroups. These disease phenotypes have been reviewed by a clinical team within FinnGen. We have applied FDR multiple testing correction to allow for overlapping phenotypes but agree that there is a chance some nominal results are of interest. However, given this more stringent threshold of significance we do have confidence in the disease and biomarker associations that we report.10) For the GDF15 plasma concentration associations with prevalent and incident cases, why was only diabetes was analyzed? At least pulmonary related diseases, as a whole, should be analyzed. This section is very informative.We thank the reviewer for noticing this point. As suggested, we have now explored GDF15 plasma concentrations in prevalent and incident cases in the top associated disease endpoints; type 2 diabetes, chronic obstructive pulmonary disease; atherosclerosis ; psychiatric disorders and malignant neoplasm of respiratory system and intrathoracic organs. We have added results to the manuscript and Supplementary File, Table S2d.11) More analysis should be done for the GDF15 and biomarker associations instead of simple descriptive analysis.th May 2022), this comment was not addressed.As advised by the editor (correspondence on 2712) If BMI is the driving force for GDF15, how to explain that GDF15 has much stronger effects than BMI in the Cox proportional hazard analysis presented in Figure 1?Thank you for raising this interesting question. Whilst we have presented evidence that BMI may be causal for changes in GDF15, it is also possible that this is not specific to BMI. Obesity (measured using BMI) was a more significant predictor of diabetes mellitus but not cardiovascular disease. GDF15 is upregulated in many diseases and is also associated with CRP, indicating it may well be a non-specific biomarker. This is also demonstrated in its effect on all-cause mortality in Figure 1(a).13) More detailed analysis with BMI could help to close the loop of BMI-GDF15-mortality.th May 2022), this comment was not addressed.As advised by the editor (correspondence on 2714) The truncated variants had no significant effects but given the subjects also had a normal allele this presumably compensated for the truncated allele. A homozygote or compound heterozygote for a non functioning allele could be highly informative but will probably be rare and hard to find. This could be further discussed.Thank you for this comment. We agree and have now included in the Results section of the revised manuscript:GDF15 LOF was found in over 300,000 patients we were not able to test this hypothesis. It may be worthwhile for future analysis to experimentally explore the effect of heterozygous LOF on GDF15 protein levels”.“It is possible that the other allele is compensating for the LOF but given that no homozygous We have additionally added the following to the discussion “… albeit only heterozygous LOF could be assessed”.


African populations are vastly underrepresented in genetic studies but have the most genetic variation and face wide-ranging environmental exposures globally. Because systematic evaluations of genetic prediction had not yet been conducted in ancestries that span African diversity, we calculated polygenic risk scores (PRSs) in simulations across Africa and in empirical data from South Africa, Uganda, and the United Kingdom to better understand the generalizability of genetic studies. PRS accuracy improves with ancestry-matched discovery cohorts more than from ancestry-mismatched studies. Within ancestrally and ethnically diverse South African individuals, we find that PRS accuracy is low for all traits but varies across groups. Differences in African ancestries contribute more to variability in PRS accuracy than other large cohort differences considered between individuals in the United Kingdom versus Uganda. We computed PRS in African ancestry populations using existing European-only versus ancestrally diverse genetic studies; the increased diversity produced the largest accuracy gains for hemoglobin concentration and white blood cell count, reflecting large-effect ancestry-enriched variants in genes known to influence sickle cell anemia and the allergic response, respectively. Differences in PRS accuracy across African ancestries originating from diverse regions are as large as across out-of-Africa continental ancestries, requiring commensurate nuance. Majara et al. show that polygenic scores generalize variably across diverse populations due to genetic differences between target and discovery cohorts. Multi-ancestry discovery GWAS typically improve prediction accuracy in underrepresented populations more than an increase in the European sample size, but not necessarily when sample sizes are imbalanced. They have the lowest life expectancies globally,,,There are also clear benefits to including African populations in statistical genetics efforts. Because humans originated in Africa, populations from Africa have the most genetic diversity among global populations,In this study, we have investigated how PRSs generalize within and among diverse African populations in simulations and with empirical genotype-phenotype data for dozens of quantitative traits. We first simulated genetic effects and computed genetic risk prediction accuracy using data from two African datasets: the African Genome Variation Project (AGVP) and the Africa Wits-INDEPTH partnership for Genomic Studies (AWI-Gen) project. We then calculated PRS using publicly available GWAS summary statistics from predominantly European ancestry populations to (1) quantify PRS accuracy for five physical and psychosocial traits among populations in the Drakenstein Child Health Study (DCHS) of South Africa, a birth cohort study; and (2) compare PRS accuracy for 34 quantitative traits across the Ugandan General Population Cohort (GPC) versus ancestrally diverse UK Biobank participants. Our results highlight the disproportionate benefits of genetic studies in diverse African populations to improve trait prediction. Further, while PRSs hold promise as biomarkers in precision medicine, a critical prerequisite is equitable accuracy in diverse populations to avoid exacerbating existing health disparities.Total counts of individuals by population and/or study are shown in 1000 Genomes Project data from the phase 3 integrated call set were accessed and used as a reference panel and for phasing and imputation.http://hail.is).Genotype data for samples from the Human Genome Diversity Project (HGDP) was publicly available on the Illumina HumanHap650K GWAS array on hg18.As described previously,,AWI-Gen is a study that investigates the relationship between genetics and the environment in causing cardiometabolic disease in sub-Saharan Africa with study participants from Burkina Faso, Ghana, Kenya, and South Africa.,,The DCHS is an ongoing, multidisciplinary population-based birth cohort study in the Drakenstein area in Paarl , that obtained ethical approval from the Faculty of Health Sciences Research Ethics Committee at the University of Cape Town (401/2009) and the Western Cape Provincial Research committee.,The rural Uganda GPC of MRC/UVRI & LSHTM Uganda Research Unit was set up in 1989 initially to monitor the HIV epidemic among adults, children, and adolescents, after obtaining ethical approval the Uganda Virus Research Institute Science and Ethics committe and the Ugandan National Council of Science and Technology. It's mandate has since expanded to include other medical conditions.https://www.ukbiobank.ac.uk/. We analyzed phenotypes that overlapped with those studied in the Uganda GPC.The UK Biobank enrolled 500,000 people aged between 40 and 69 years in 2006–2010 from across the country, as described previously.As described previously,Next, we further partitioned African ancestry individuals using the same random forest approach as above but without further probability thresholding using African ancestry reference data from AGVP, HGDP, and the 1000 Genomes Project. We partitioned these reference data into UN regional codes with an additional region for Ethiopian populations given their unique population history and collapsing in AGVP data , as shown in We used the Ricopili pipeline to conduct pre-imputation quality control (QC) and perform phasing and imputation for AGVP and the Uganda GPC.Only SNPs with high imputation quality (INFO >0.8) were considered for principal-component analysis. We computed the first 20 principal components using plink with the --pca flag for autosomal SNPs MAF >0.05 and individual missingness <0.05.h2 = 0.1, 0.2, 0.4 and 0.8) for both AGVP and AWI-Gen as follows.We used two independent simulation strategies for two African datasets: AGVP and AWI-Gen. The choice of simulation strategy was informed by the sample size of each dataset. We used the simulation strategy previously used by Scutari et al.We randomly assigned an effect size to 5, 20, 100, 2,000, 10,000, and 50,000 causal variants, respectively. We then calculated an individual’s “true” polygenic risk as the sum of all causal effects using the --score flag in PLINK v1.07B.We then conducted GWAS for the simulated phenotypes by splitting the AGVP dataset into three groups broadly representing the three geographical areas from which samples were obtained: East (n = 589), West (n = 517), and South Africa n = 186, . To alloWe assigned genetic effects to variants based on their minor allele frequency. The effects were calculated based on the relationship between effect size and minor allele frequency as shown by Schoech et al.r2 value greater than 0.1 using in-sample linkage disequilibrium (LD) and within 500 kb of each other in n PLINK v1.07, were obtained for each discovery cohort. The effect sizes from these SNP sets were used as weights to compute PRSs for all corresponding target datasets for a range of p values . PRS was calculated as the sum of all SNPs multiplied by their effect sizes.For each discovery cohort in AGVP and AWI-Gen, we obtained independent SNP sets by clumping SNPs from corresponding summary statistics files with an https://data.broadinstitute.org/alkesgroup/LDSCORE/). Due to the difference in study design and heritability estimation methods used for UK Biobank and Uganda GPC, we could not directly compare heritability estimates between the two cohorts. For more comparable estimates, we computed heritability estimates across the 34 quantitative traits in both the Ugandan GPC and UK Biobank using a randomized multi-component Haseman-Elston estimator . Namely, in UK Biobank, we filtered genotypes to those outside the MHC region that were defined with MAF ≥0.01 for which we did not observe significant deviation from Hardy-Weinberg equilibrium (p_hwe ≥ 1e-7) and only used genotypes that passed these criteria across all ancestry groups. For the Ugandan GPC analysis, we applied the same filters as above . We also restricted the analysis to SNPs with imputation INFO score >0.3 to match the approach taken for the GWAS conducted by Gurdasani et al.,2, age2 ∗sex, and the first 20 genotype PCs in each population. LD score bins in both cohorts were computed as membership in quartiles of the LD score distribution. MAF bins in both cohorts were defined as MAF ≤0.05 and MAF >0.05.To account for differences in heritability as a function of LD and MAF, we performed multi-component analyses with a 4 × 2 grid of LD and MAF bins defined in Ugandan GPC and per-population in UK Biobank. LD scores in the Ugandan GPC were computed in LD score regression using all imputed SNPs with MAF >0.005 from unrelated samples, while those in the UK Biobank were computed using SNPs with INFO >0.8, MAC >20 with subsequent covariate correction for age, sex, age∗sex, age2, age2 ∗sex, and the first 20 genotype PCs in each cohort and in each population in the UK Biobank. We ran RHE-mc with 50 random vectors and 100 jackknife blocks in the Uganda GPC and among UK Biobank non-EUR populations; and used 10 random vectors with UK Biobank European samples due to high computational complexity.We included standard GWAS covariates as fixed-effects covariates for heritability estimation in both Ugandan GPC and UK Biobank, namely age, sex, age∗sex, ager2 = 0.1 and a window size of 500 kb with discovery cohort population-specific reference panels. We calculated PRS using plink2 with the --score and --q-score-range flags for AGVP simulations and DCHS. We wrote custom scripts in hail (http://hail.is) to calculate PRS in the Uganda GPC and UK Biobank data due to the larger sample sizes . For AGVP simulations and DCHS , we included the first 10 PCs as covariates when computing the partial R2. Specifically, we compared two models:To evaluate prediction accuracy, we calculated incremental 0: Phenotype ∼ covariates.H1: Phenotype ∼ PRS + covariates.HR2 calculates the change in R2 between H1 and H0, indicating the change in model accuracy attributable to the PRS. We used adjusted R2, which ensures that the model that includes PRS does not outperform the model without PRS simply because more terms were included. All error bars show 95% confidence intervals calculated from bootstrapping. Specifically, for each iteration of 100 bootstrap replicates, we resampled with replacement each individual’s full set of phenotypes, covariates, and polygenic scores, then ran the same models described above. The 95% confidence interval was determined by the 2.5% and 97.5% quantiles.The incremental R2 in the population of interest versus the maximum R2 in the European baseline comparison . Across traits, we computed median absolute deviation (MAD), i.e., the median of the absolute deviations from the median.We compared PRS accuracy across populations by computing relative accuracies (RAs) with respect to a baseline European ancestry PRS. For pruning and thresholding PRS, we computed RA as the ratio between the maximum R2 for pruning and thresholding or R2 for PRS-CS versus the maximum R2 in the European population for each trait.We compared the prediction accuracy of the pruning and thresholding method to that of PRS-CS, a Bayesian method that has been shown to improve PRS prediction accuracy across diverse populations.,E is the predicted accuracy, h2 is the heritability estimates, M is the number of independent SNPs , and N is the sample size .To evaluate the efficacy of PRS accuracy given the varying genetic architecture of the quantitative traits we assessed here, we compared the PRS accuracy we observed with the accuracy that would be predicted from theoretical models.We used plink2 to conduct inverse variance-weighted meta-analysis across GWAS summary statistics with the --meta-analysis option.All PRS calculations required an LD panel for clumping. Our analyses used in-sample LD where feasible and reference panel data as a proxy with ancestry matching from the 1000 Genomes Project phase 3 data when individual-level data were unavailable. We weighted the ancestral representation of each population per trait matching at the continental level. We matched individuals as follows.We then used the maximal number of individuals available when weighting proportionally to construct this reference panel. For example, in the meta-analysis of height across the UK Biobank, Biobank Japan (BBJ), and Population Architecture Using Genomics and Epidemiology (PAGE) cohorts, UK Biobank has the largest sample size in the discovery cohort , so all Europeans from 1000 Genomes were included in the reference panel (n = 503), then a random sampling of EAS, AFR, and AMR individuals were included proportionally to the overall diversity of the discovery cohorts in the meta-analysis.Our study uses both simulation-based and empirical approaches to evaluate the generalizability of PRS across diverse African ancestry populations. Abbreviations are in 2 = 0.1, 0.2, 0.4, and 0.8), then conducted independent GWASs for each scenario in East and West African ancestry populations . The scores in the South target sample were comparable when using East- or West-derived summary statistics .We used two separate simulation strategies for AGVP and AWI-Gen depending on their sample sizes . Given tulations . We calcvariants A, as indgligible , as evidgligible A. The wiFor AWI-Gen, we used the commonly used infinitesimal simulation strategy for quantitative traits. We simulated quantitative traits by assigning genetic variant effects based on their minor allele frequency in accordance with the relationship between effects and minor allele frequency established by Schoech et al.While our simulations have shown that PRSs generalize poorly across Africa due to substantial genetic diversity and differences across the continent, there is also considerable genetic and environmental diversity within regions and countries. We quantified PRS accuracy for a range of measured phenotypes in mothers genotyped in the DCHS cohort in South Africa, including several sociodemographic, physical/biomedical, and psychosocial risk traits . The DCHR2 = 0.099, 95% bootstrapped CI = 0.012–0.18, p = 1.5e-7 versus R2 = 0.021, 95% CI = −0.031 to 0.043, p = 5.27e-3, respectively). We also expect that PRS accuracy increases with decreasing African ancestry within the Mixed ethnic group as has been shown previously in admixed African populationsR2 = 0.091, 95% CI = −0.04 to 0.17, p = 6.4e-4 in lower half of PC1 with more African ancestry versus R2 = 0.12, 95% CI = −9.0e-4 to 0.21, p = 5.7e-5 with more out-of-Africa ancestry), although small sample sizes limit definitive comparisons (n = 137 in each PC1 bin). Our results are consistent with variable prediction accuracy among diverse African ancestry groups within South Africa and insignificant prediction in African populations for all but the most heritable and accurately predicted traits elsewhere. Notwithstanding these findings, the sample used for these analyses is relatively small and does not represent the larger South African population and some of the traits are greatly impacted by pregnancy, for example, pregnant women are less likely to drink and smoke than the general public. In addition, in contrast to the discovery datasets that include males and females, DCHS is a female-only cohort that has both a lower and narrower age range, which could impact PRS accuracy for the traits where age plays a key role.Across all genetically predicted phenotypes, only height was significantly predicted . We predWe next investigated phenotypic similarities within and across the Uganda GPC and UK Biobank participants because these are two of the largest cohorts with dozens of traits measured in African ancestry individuals. We first considered overall cohort differences between these cohorts: the Uganda GPC enrolled participants using a house-to-house study design and generated genetic data on 5,000 adults from rural villages in southwestern Uganda,materials and methodsZ statistic = 0.73, p < 1e-4). The correlations among phenotypes are slightly higher overall in the Uganda GPC than in UK Biobank, both among related and unrelated individuals , while the average variance explained across all traits was less than 1% (mean R2 = 0.007). To assess the relative effects of ancestry versus cohort differences on decreases in prediction accuracy across populations, we next withheld 10,000 European ancestry individuals from UK Biobank for use as a target cohort, reran all GWASs, then used individuals with diverse continental ancestries in the UK Biobank as target populations  ∗ 100%) due to MAF and LD in the subcontinental African ancestry groups in the UK Biobank. As expected, LOA followed an inverse trend to prediction accuracy, i.e., LOA increased with genetic distance between the discovery and target cohort . LOA wasR2 = 0.017, SD = 0.013 versus mean R2 = 0.012, SD = 0.010, respectively, ,To compare prediction accuracy among similar ancestry participants from different cohorts, we next computed PRSs for 34 traits using GWAS summary statistics from UK Biobank Europeans in two target populations: UK Biobank participants with East African ancestry versus Uganda GPC. As expected, prediction accuracy in these populations is very low across all traits in both cohorts and only slightly higher in the UK East African ancestry individuals than in the Uganda GPC individuals ,TMPRSS6 ,,PIEZO1 and CDT1 MED24 , rs58650325 near the high affinity immunoglobulin (Ig)E receptor FCER1A that initiates the allergic response , and rs11533993 in CDK6 . Thus, genetic architecture and population genetic considerations are important to bear in mind when considering the generalizability of polygenic scores.We find that PRS accuracy improvements from higher diversity in the discovery cohorts vary across traits, with the largest increases seen in MCHC and WBC particularly in AMR and AFR populations B. We sea,,PRSs have been proposed as genetic biomarkers for use in preventive medicine,We demonstrate looming challenges for applying current PRS in African ancestry populations—because relatively few genetic studies have been conducted in African populations coupled with the lack of out-of-Africa population bottlenecks, PRS accuracy is low but widely variable. Differences in PRS accuracy across diverse African ancestries from different regions can be larger than across out-of-Africa continents. This is particularly problematic, as widely used algorithms that guide health decisions already have ingrained racial biases,APOL1 and G6PD have been shown to contribute to especially high risk of chronic kidney disease and to missed diabetes diagnosis, respectively.,,Another advantage of using GWASs from globally diverse populations to compute PRS is the routine inclusion of population-enriched variants. Clear examples such as African-enriched variants in ,,,In addition to reduced PRS accuracy with ancestral distance from GWAS cohorts, genetic nurture, social genetic, and environmental effects can also contribute to low portability of PRS across populations,,,,,,,,,,While PRSs currently have limited portability, increased diversity in genetic studies is already decreasing prediction accuracy gaps across populations.,,Beyond expanding on diversity by increasing the number of study participants in large-scale studies, it is equally important to diversify researchers working on genomics studies. Currently, the vast majority of researchers in genomics studies are of European ancestry,Previous studies that have examined PRS accuracy across globally diverse ancestry groups have demonstrated that accuracy is lowest in African ancestry samples. However, the extent to which this accuracy varies within African ancestry populations has not been previously investigated. Our findings that prediction accuracy varies by African ancestry populations is a clear reflection of the vast genetic diversity of the continent. It is therefore critically important to create well-powered GWASs that reflect the full range of diversity within Africa.http://jenger.riken.jp/en/result. GWAS summary statistics for the Population Architecture using Genomics and Epidemiology (PAGE) study were accessed through the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).All data used in this study are publicly available. Data from the African Genome Variation Project was accessed by combining EGAD00010001045, EGAD00010001046, EGAD00010001049, EGAD00010001050, EGAD00010001051, EGAD00010001052, EGAD00010001053, EGAD00010001054, EGAD00010001055, EGAD00010001056, EGAD00010001057, and EGAD00010001058. The Drakenstein Child Health Study is committed to the principle of data sharing. De-identified data will be made available to requesting researchers as appropriate. Requests for collaborations to undertake data analysis are welcome. Uganda GPC genetic data used in this paper were accessed through EGAD00010000965 and phenotype data were accessed via sftp from EGA (reference: DD_PK_050716 gwas_phenotypes_28Oct14.txt). We accessed data from the UK Biobank with application 31,063. BioBank Japan summary statistics were accessed from https://github.com/armartin/africa_prs.All code used in analysis is available here:


In this study, we estimated genetic parameters of 46 linear morpho-functional traits, and analyzed the relationship between two coat color traits  and other linear morpho-functional traits within the breeding program of Pura Raza Menorquina horses, whose studbook only permits the use of black-coated animals with a small quantity of white marks as breeding stock. A total of 772 records from 333 animals were analyzed to estimate genetic parameters for 46 linear traits scored by four appraisers using seven classes. Heritability values for morpho-functional traits were low to medium and matched the range in the bibliography. Medium heritability values were obtained for both coat color traits (0.36 for QB and 0.23 for WM). Genetic correlations between coat and morpho-functional traits ranged between 0.015 and 0.816 in absolute value for QB and between 0.014 and 0.638 in absolute value for WM. The highest correlation values were obtained between QB and upper neck line (0.816) and between WM and form of the hoof (0.638). It was observed that the animal group with low and the group with high breeding values for QB and WM had a clear differentiation of the other mor-pho-functional traits.The studbook of Pura Raza Menorquina horses only permits the use of black-coated animals with a small quantity of white marks as breeding stock. Its breeding program uses linear morpho-functional traits as selection criteria. Our aim was to estimate the genetic parameters of linear morpho-functional traits, and reveal relationship of quality of black coat color (QB) and percentage of white marks (WM) with the other morphological and functional linear traits in this breed. A total of 46 linear traits were scored by four appraisers using seven classes, with a total of 772 records from 333 animals (≥4 years old). Univariate animal models using a Bayesian approach were used, with a pedigree of 757 animals. Sex (two) and appraiser-season (13) were included as fixed effects, age as a linear covariate, and permanent environmental and additive genetic as random effect. The heritabilities of the morpho-functional traits were low to medium (0.09–0.58) and matched the range in the bibliography. Heritabilities for coat color traits were 0.36 for QB and 0.23 for WM. The highest genetic correlations were obtained between QB and upper neck line (0.816) and between WM and form of the hoof (0.638). The negative signs of most of the genetic correlations between WM and the functional traits is also remarkable, contributing to the selection of functional traits against the presence of white marks in this population. A clear genetic differentiation was observed between animals with better breeding values for QB and WM, corroborated by a study on founders. In conclusion, QB and WM could show different genetic backgrounds. The current conformation of horses is the result of both natural and breeders’ selection, and the traits evaluated for each breed depend on their breeding purposes . ConformThe genetic evaluation of conformation traits is of major importance in horse selection because it is a tool for indirect performance selection. This is because the heritability estimates for conformation traits are often higher than those estimated for performance traits. Some authors  have affThe current trend for breeding sport horses combines selection for conformation and performance. Unfortunately, most of the information on sport performance only becomes available later in the horse’s life. For this reason, the selection of riding horses is based primarily on other related traits, which are available at a younger age and have higher heritability values and adequate correlations with the selection traits  (such asThe Pura Raza Menorquina horse (PRMe) is an endangered native breed established in 1988. However, they were traditionally bred and located on the island of Menorca . Originally, they were mainly used as draught animals in agriculture and forestry. However, it is a highly versatile breed with excellent qualities  that make it a highly-prized animal whether for dressage or as a saddle horse, a work horse, a light cart-horse or even a sport horse . NowadayThe last official update of the Spanish Ministry of Agriculture database  reportedThe official breeding program of PRMe horses (with conservation and selection aims) and their current uses have probably influenced their body conformation and type, as well as their movements and performance. Thus, Menorcan breeders tend to select animals based on conformation to achieve good performance for Menorcan and Classic dressage . From a The main objective of this study was to estimate genetic parameters of morphological and functional traits, and analyze relationship of quality of their black coat color (QB) and the percentage of white marks (WM) with the other morphological and functional linear traits.For this analysis, we used the linear morpho-functional data of PRMe horses provided by the Asociación de Criadores y Propietarios de Caballos de Raza Menorquina (ACPCRMe). To avoid subjectivity to an ideal type in the comparison, the overall score was replaced by several simple traits, which are linearly evaluated on a scale from one biological extreme to the other; in this way, linear evaluation does not grade an animal, but rather describes it .Linear type traits scored by four skilled horse appraisers with the same evaluation criteria over nine years of evaluation (2013–2021) were considered in the current analysis. A total of 772 records from 333 animals  were recorded for 46 linear morpho-functional traits in the studs, in sport competitions, and in morphological events held for PRMe horses. Thus, the average number of records per animal was 2.32 and the average number of records per stud was 5.51. The animals were at least four years old, and the average age of the recorded animals was 8.44 years. The animals analyzed represented 12.9% of the population registered in the official Menorca Horse Studbook, which were active and located in Spain in 2021, thereby constituting the nucleus for selection and the majority of the population of PRMe horses all around the world. In this way, the results obtained in this study can be considered descriptive of the current status of this genetic resource and can be used to control its development in future years.To collect the information, a structured score sheet with a scale of seven categories  was used by appraisers, in which the extremes represented the biological extremes of the population for the linear traits see . Both exFor data collection, the QB was defined using the same criteria as Smith , who desPreliminary analyses of variance were carried out using the univariate GLM procedure in SAS software v.9.4  to asses2 of surface and the same weather conditions.The owner stud of the animals was not included in the model because no statistically significant differences were detected for most of the analyzed traits. This could be caused by (1) the similar management systems of the animals in the different studs  and (2) the geographic nearness of the different studs because all of them were located on Menorca island with approximately 700 kmy was the vector of observations for a particular trait of the analyzed traits; µ was the overall mean; 1 is the vector of ones; b was the vector of the fixed effects ; u was the vector of random additive genetic effect; pe was the vector of random permanent environmental effect of the animal; and e was the vector of random residual effect. X was the incidence matrix relating observations to fixed effects, Z was the incidence matrix relating observations to additive effects, and W was the incidence matrix relating observations to random permanent environmental effects.The heritability values were calculated based on the variance components estimated using univariate animal models with a Bayesian approach via Gibbs sampling using the GIBBSF90+ module of the BLUPF90 software . The folAlso, bivariate models were implemented to estimate the genetic correlations between the two coat color traits (QB and WM) and the other morpho-functional linear traits. The bivariate models fit the same effects as the univariate models.The Gibbs sampler was run for 1,000,000 rounds, with the first 100,000 considered as burn-in and then every 10th sample saved for later analysis. Posterior means and standard deviations were calculated to obtain estimates of (co) variance components. Convergence of the posterior parameters was assessed by visual inspection of trace plots of posterior distributions generated by the Coda R package . The pedigree data for the estimation of the genetic parameters was composed of 757 animals born between 1961 and 2018 , constituting a total of five generations of animals registered in the official Studbook of PRMe horses managed by the ACPCRMe. Finally, four subpopulations of animals were created according to their EBVs for each coat trait analyzed. Subpopulation A included animals with an EBV belonging to the better 25% for each trait ; subpopulation B included the remaining animals with an EBV belonging to the lower 75% . To estimate the EBVs of WMs, a change of the scale was needed because it varied between 1  and 7 . Finally, ENDOG software  was usedDescriptive statistics of the 46 linear traits analyzed in PRMe horses are reported in In the morpho-functional evaluation of the PRMe horses, a total of seven classes were used by the appraisers to assess the traits, the same number as was used for morpho-functional evaluations in Thoroughbred horses  and for The coefficient of variation (CV) is considered the most important measure of variation. It generally assumes that the higher the phenotypic variation of traits, the greater the genetic variation, which guarantees a sufficient selection response in populations . The estFor morpho-functional traits, the lowest CV was observed for the upper neckline 10; 15.31%) within the morphological traits and for walking activity  within the performance traits, showing that their phenotypic variation is limited biologically in this population. On the other hand, the highest CV was estimated for the form of the withers 13; 32.89%) within the morphological traits and trotting suspension 46; 25.45%) within the performance traits, showing that there is higher phenotypic variation in these traits than in other traits. The CVs obtained were in the range of those reported for linear traits in Dutch Warmblood Riding horses , .45% with% within p < 0.05) for some analyzed traits. Permanent environment, combination of the appraiser and the season, and sex were significant for most of the analyzed traits and were therefore included in the genetic model used in this study. The coefficient of determination (R2) was also estimated for all of the traits, with values higher than and close to 0.55. In this way, the model accounted for over 60% of the variance for most of the linear morpho-functional traits analyzed in the PRMe population.The influences of the different non-genetic effects are shown in Coat color is one of the most noticeable animal features and has interested and intrigued breeders for centuries . It is bA horse’s coat color is generally understood as a qualitative trait with Mendelian inheritance. Here, black coat color is determined by a recessive homozygote genotype at the Agouti locus and at least one dominant allele at the EXTENSION locus (aa E-) . HoweverThe heritability values of the 46 linear morpho-functional traits analyzed in the PRMe population are shown in Medium heritability values were obtained for the linear coat color traits, 0.36 for QB (1) and 0.23 for WM (2), which evidenced the level of selection that can be carried out for both linear traits related to the coat color in PRMe horses. These values were in the range of those reported for the quality of black coat color in Old Kladrub horses (0.14–0.37 ) and forA previous study  postulatBesides the existence of possible pleiotropic effects associated with specific coat color and some conformation , performHeritability values estimated with the univariate animal models for the morphological traits obtained in the present study were of low to medium range 0.09–0.58), showing the higher values of 0.58 for head profile (6), 0.44 for head length (4), and 0.41 for thorax depth (16). In general, the heritability values we obtained were in the range of those reported for linear conformation traits in other horse breeds, which ranged between 0.03 and 0.68 , to The heritability values estimated for the functional traits obtained in the present study were also of low to medium range, with values of 0.09 for walking activity (38) and walking clarity (39), 0.32 for walking suppleness 41), and 0.33 for walking amplitude (40). The values obtained for these kinds of linear traits were lower than those reported in the reviewed bibliography for linear functional traits in other horses, ranging between 0.18 and 0.52 in sport horses in the Czech Republic [, and 0.3Genetic parameters such as heritability are influenced by gene frequency, estimation method, statistical model, and trait nature. Since there may have been differences in the factors used in each study, the slight variations in values between the present study and previous studies might be attributable to those differences. The medium to high estimated heritability for the traits in the current study indicates that genetic improvement would be possible in these traits and allow us to foresee the possibility of making good genetic progress through the breeding program.Understanding the relationships between morphological traits is extremely useful in animal breeding for determining both the breeding criteria and the possible breeding response of selection programs . HoweverFor several centuries, behavior, conformation, performance, and suitability characteristics of horses have been attributed to coat color in different populations. For example, coat color influences the conformation traits analyzed in Old Kladrub horses  and in PIn general, both color traits showed low to high correlations with the 44 morpho-functional traits. The highest values obtained for QB were with upper neck line , shape of the withers , cannon bone perimeter , back length , and head profile . The highest correlations estimated for WM were with form of the hoof , shoulder angle slope , fore knee perimeter , fore-hoof front view , shape of the withers , buttock length , and walking clarity . The existence of an important relationship between the coat color traits and other linear traits could be caused by a pleiotropic effect, which should be analyzed with the adequate tools.It is important to note that for QB (1), 37.14% of the genetic correlations with morphological traits were negative, and none of them were correlated with functional traits. For WM (2), 48.57% of the phenotypic correlations with morphological traits were negative whereas 88.89% of the correlations with functional traits were negative. However, in the PRMe population, animals with or without a lower percentage of white marks are required as breeding stock. Therefore, these negative correlations are very interesting in the breeding program.The genetic correlation between both coat color traits (QB and WM) was −0.272. That means that selection to improve the quality of black coat color implies a decrease in the percentage of white marks, as is desired in this population.Finally, for the graphic representation of Mahalanobis distances, animals were grouped by their EBVs for both traits related to coat color included in the analysis, taking into account all of the morpho-functional linear traits analyzed. The population’s average relatedness was 2.28%, the average inbreeding was 0.6%, and 8.1% of all the pedigreed animals presented some level of inbreeding. The results are shown in The heritability values obtained for the morpho-functional traits related to coat color in PRMe horses indicate that selection is feasible, and that these traits could be improved in the official Breeding Program. The genetic correlations of coat color traits with linear morpho-functional traits evidence the existence of genetic and physiological mechanisms controlling them, showing that selection for one of these traits could have an influence on  the other traits of interest. The existence of pleiotropic effects between the coat color traits and some morpho-functional linear traits could be also suspected because of the high genetic correlations obtained between some morpho-functional and coat color analyzed traits. The study of Mahalanobis distances showed that the inheritance of the quality of coat color could be linked to the genetic group and determined by modifying genes. In this sense, a genomics analysis could have an interesting contribution to confirming our results in the future.