Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits  conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip ( Polygenic risk scores (PRS) are generally defined as the sum of an individual’s additive genetic risk for an outcome or disease of interest . Early gThe Population Architecture using Genomics and Epidemiology (PAGE) Study, supported by the National Human Genome Research Institute (NHGRI), was first established to generalize GWAS findings in diverse populations . The PAGThe genetic associations identified for lipid traits in the PAGE Study provide a still unique opportunity to calculate and apply multi-population lipid trait PRS for diverse populations. To seize this opportunity, we accessed clinical data from electronic health records (EHRs) linked to a biorepository with genetic data for almost 12,000 African American patients . Here, wWe accessed an existing and previously described dataset  of de-idvia a one-way hash that combined make re-identification difficult and unlikely , the majority of which are from African American or Black patients  codes, Current Procedural Terminology (CPT) codes, problems lists, and laboratory values) and unstructured  data available in the EHR.  Race/ethnicity in BioVU was administratively assigned, and these assignments are highly correlated with genetic ancestry for some  but not all  groups  of each patient’s serum HDL-C, LDL-C, TG, and TC was extracted and recorded as either a “pre-medication” or “post-medication” value. Pre-medication values were defined as free of evidence of concurrent lipid lowering medication usage. As previously described (®), fluvastatin (Lescol®), lovastatin , pravastatin (Pravachol®), rosuvastatin calcium (Crestor®), simvastatin (Zocor®), lovastatin + niacin (Advicor®), atorvastatin + amlodipine (Caduet®), and simvastatin + ezetimibe (Vytorin™)), selective cholesterol absorption inhibitors (ezetimibe (Zetia®)), resins , colestipol (Colestid®), colesevelam Hcl (WelChol®)), fibrates (gemfibrozil (Lopid®), fenofibrate , clofibrate (Atromid-S)), and niacin. Fasting status is unknown. Heights and weights closest to the first mention of the lipid trait were extracted to calculate body mass index . Prevalent cardiovascular disease was defined as mention of heart attack keywords in the problems list , CPT codes  have been described by Buyske et al. 2011 and more recently by The Illumina Metabochip was genotyped by Vanderbilt Technologies for Advanced Genomics (VANTAGE), formerly the Vanderbilt University Center for Human Genetics Research DNA Resources Core, for the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, a study site of PAGE I . Genotypp ≤ 5  ×  10−8)  for 42, 34, 50, and 46 HDL-C, LDL-C, TG, and TC-associated SNPs (k), respectively. PRS were weighted (PRSw) using the absolute value of the betas (|β|) for the corresponding associations published by the PAGE Study  . After rGE Study .2), stage 2 (60–89 mL/min per 1.73 m2), and stage 3 (<60 mL/min per 1.73 m2) for the limited PheWAS. When necessary, laboratory values were transformed as the natural log of the quantitative value plus one per the PAGE I study PheWAS protocol  were generated as previously described . We testprotocol . All Pheprotocol  and PLIN2; ±8.52 kg/m2 standard deviation) considered obese. Average pre-medication and post-medication lipid lab values were within the range expected for a general US adult African American population  and total cholesterol (rs8106922) each, all but one of the associated SNPs were in the expected direction of effect.We first performed unadjusted  and adju and PCs –4. Compa and PCs , all assp < 0.05 adjusted for sex, age, BMI, and PCs  compared with pre-medication lipid labs (20), most likely due to differences in sample size (rs1864163 (HDL-C), rs688 (LDL-C), and rs4407894 (transformed TG)  versus. Of thesMaterials & Methods. Among unadjusted and adjusted tests of association, none were associated with their respective lipid traits at p < 0.05 . The association was no longer nominally significant when adjusted for LDL-C levels (p > 0.05).To explore the potential clinical utility of lipid PRS, we tested each lipid weighted PRS for association with CVD and T2DM . In thisp < 0.05 . For LDLp < 0.05) were identified for HDL-C PRS and albumin creatinine ratio; serum creatinine and LDL-C and TC PRS; and stage 2 eGFR and TC PRS  and applied to African Americans is limited. A recent assessment LDL-C, HDL-C, TG, and TC PRS derived from European, multi-ancestral, and sub-Saharan African discovery meta-analyses all demonstrated strong associations in the expected directions for their respective lipid traits in an independent sample of 7,103 participants from the Africa Wits-INDEPTH Partnership for Genomic Studies (AWI-Gen) cohort . Of notee.g., LDL-C), differ by the number of variants included and how variants are chosen. PRS also differ by the discovery data from which they draw both variants and weights, much of which is still focused on European-descent populations. Finally, there are no standards on the evaluation and reporting of PRS performance. In the present study, we selected index variants associated with lipid traits in a diverse population that includes African Americans and calculated unweighted and weighted PRS using the absolute values of the betas reported in the discovery study. We applied the PRS to an independent sample of African American patients and reported the betas and p-values from unadjusted and adjusted tests of association. Collectively, the comparison challenges we experienced here highlight the need to develop approaches to standardize PRS being developed for implementation in a clinical setting to benefit patients  are plotted along the y-axis using Synthesis View. The direction of the arrows corresponds to the direction of the beta-coefficient. The significance threshold is indicated by the red line at p = 0.05. Also plotted are the betas and the coded allele frequencies (CAF).Lipid labs were extracted from EHRs and represent the first mention of the laboratory value free of evidence of concurrent lipid lowering medication usage (“pre-medication”). Each SNP was tested for an association with each pre-medication lipid lab using linear regression assuming an additive genetic model. SNP genomic location is given on the Click here for additional data file.10.7717/peerj.14910/supp-2Supplemental Information 2x-axis, and p-values (−log10 transformed) are plotted along the y-axis using Synthesis View. The direction of the arrows corresponds to the direction of the beta-coefficient. The significance threshold is indicated by the red line at p = 0.05. Also plotted are the betas and the coded allele frequencies (CAF).Lipid labs were extracted from EHRs and represent the first mention of the laboratory value with evidence of concurrent lipid lowering medication usage (“post-medication”). Each SNP was tested for an association with each post-medication lipid lab using linear regression assuming an additive genetic model adjusted for age, sex, body mass index, and the first 10 principal components. Triglyceride levels were transformed  prior to tests of association. SNP genomic location is given on the Click here for additional data file.10.7717/peerj.14910/supp-3Supplemental Information 3x-axis, and p-values (−log10 transformed) are plotted along the y-axis using genome build NCBI36/hg18 in Synthesis View. The direction of the arrows corresponds to the direction of the beta-coefficient. The significance threshold is indicated by the red line at p = 0.05. Also plotted are the betas and the coded allele frequencies (CAF).Lipid labs were extracted from EHRs and represent the first mention of the laboratory value with evidence of concurrent lipid lowering medication usage (“post-medication”). Each SNP was tested for an association with each post-medication lipid lab using linear regression assuming an additive genetic model adjusted for age, sex, body mass index, and the first 10 principal components. Triglyceride levels were transformed  prior to tests of association. SNP genomic location is given on the Click here for additional data file.10.7717/peerj.14910/supp-4Supplemental Information 4p ≤ 5.0 × 10−8 with high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG) by (We identified SNPs genotyped on the Metabochip that were reported as associated at  (TG) by . Shown pClick here for additional data file.10.7717/peerj.14910/supp-5Supplemental Information 5p-value , beta as the effect size (es), and coded allele frequency (caf). Summary statistics are given for unadjusted and adjusted tests of association between each SNP and lipid trait. Adjusted models are models adjusted for age, sex, body mass index, and principal components.Results of tests of association are given here formatted for Synthesis View. These summary statistics include SNP ID (rs number), chromosome (chr) and position (NCBI36/hg18), Click here for additional data file.


Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM).Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage.The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF The online version contains supplementary material available at 10.1186/s12864-023-09415-0. Genotype imputation has transformed the conduct of genome-wide association studies (GWAS). By imputing unobserved genotypes into sample sets that have relatively limited coverage of variants across the genome, contemporary GWAS can now query tens of millions of genetic variants in a single study. There is a mature literature on methodologies for imputation of genotype values –3. To imype data –11.Imputation is stochastic in nature, and therefore imputed genotypes are not perfect proxies for observed values. Standard imputation software outputs posterior probabilities of each possible genotype along with metrics of the confidence with which a genotype has been imputed. Many of the statistical methodologies for analyzing associations in this context have focused on ways to integrate uncertainty in genotype imputations, whether through Bayesian  or frequHere, we show that the computationally expensive unconditional implementation of MI (described in ) resultsWe first assessed type I error control through large-scale simulations see . We simup-values  represented the true genotypes see . Table 1n = 20,000 samples  showed the biggest discrepancy in results with MRM (p = 2.2 × 10–6), MI SMCFCS (p = 1.25 × 10–6), and Dosage (p = 1.9 × 10–7) providing larger signals  compared to Unconditional MI (p = 3.2 × 10–3). Even for a rare variant with higher imputation quality , again Unconditional MI displayed the weakest signal .To compare methods using a real-data example of true positive single nucleotide variant (SNV)-trait associations with rare and poorly imputed variants, we considered three variants  in the p-values) as imputation quality decreased. For the first time, we present a different approach to multiple imputation in the context of genetic association studies with imputed genotypes. By conditioning on the outcome variable, we implemented the SMCFCS approach of Grey [In this study, we compared the performance of Unconditional MI to three different methods, including a new conditional MI, i.e., MI SMCFCS, for conducting association testing with imputed genotype data. The Unconditional MI method as described in Palmer and Pe’er 2016  performe of Grey  to perfoAPOC3 gene, we also show that Unconditional MI provides the weakest association signal compared to Dosage, MI SMCFCS, and MRM.Our conclusions add to the results from Palmer and Pe’er 2016 regarding the use of Unconditional MI in imputation-based GWAS. Palmer and Pe’er 2016 focused on common variants (MAF > 0.05) with high confidence imputation scores and compared the relative ranking of SNVs under both null and alternative hypotheses. Under these conditions, they found that Unconditional MI properly ranks variants more successfully than other methods. But their study did not perform standard type I error or power simulations as was done here. Our simulations clearly demonstrate the overly conservative performance of Unconditional MI under most settings, especially for low-frequency variants  with large effects. Our implementation of MRM and MI SMCFCS was also very computationally expensive, without any gain in performance under realistic simulation settings. Overall, we observed that for variants with MAF Our study was limited to comparisons of four methods  as we did not include the methods implemented in SNPTEST . However, the focus of our work was to evaluate the performance of multiple imputation methods and for this purpose, we believe that Dosage and MRM were sufficient for our comparisons. As in Zheng et al. , we founo expand  or SKAT o expand ).We compared the performance of four different methods for incoporating imputation uncertainty into statistical tests of association: Dosage, MRM, unconditional MI, and conditional MI. The Dosage, MRM, and unconditional MI approaches all performed similarly across a range of MAFs and imputation qualities and in a real data analysis. However, we found that the unconditional MI approach was overly conservative for variants with low imputation quality (Rsq < 0.8) or low frequency (MAF < 0.01) and we do not recommend its use in association testing of imputed genotypes.We evaluated four different approaches to analyzing imputed genotypes for associations with both binary and quantitative traits; namely Dosage, MI SMCFCS, MRM, and Unconditional MI. Briefly, we let To model the association with Dosage, we take the expectation across the posterior probabilities where the dosage for each To model the association using Unconditional MI, we use a random number generator to impute genotypes based on their posterior probabilities. Let ’s rules  to produG and Y, but we do not observe G directly. Instead, we observe a version of G subject to error, denoted by our dosage variable D. In the following, we assume classical measurement error, i.e., Y and G. In this setting, we can consider a conditional imputation of G by the following typical imputation model: th imputed value for f, with mean =f using only observed data, as outlined in Keogh and Bartlett 2019 [f is defined as a posterior distribution given a likelihood and a prior distribution. Model parameters https://github.com/jwb133/smcfcs).Unconditional MI does not condition on any other covariates or outcome variables. In order to improve on this unconditional imputation, we reframe the analysis of imputed genotypes as a measurement error problem and can therefore use the value of the observed outcomes to provide more accurate imputations. In this framework, we are interested in making inferences about the assocation between nd White , the Mthett 2019  and deveett 2019 . Brieflyf as in the above, we must assume that there are two noisy measurements for G. Here, we use the genotype dosage D as well as the best-guess genotypes based on the imputation posterior probabilities, denoted W. Our rationale for using the best-guess genotype as our second noisy measurement was twofold: (i) it is a convenient measurement of G that is readily available from genotype imputation software; and (ii) when f would be relatively small, and the multiple imputations of f would be relatively large, and the multiple imputations of D and W should ideally be independent (in our case they are clearly dependent), the SMCFCS procedure is apparently robust in this context as it provides reasonable results  with the imputation Rsq . We calculated the percent difference between the imputation Rsq and the true Rsq and only considered variants in our simulations for which the percent difference was less than 20%. This resulted in a total of 181,292 exonic variant from chromosomes 1–22 with MAF p-values of the variants in each MAF and Rsq bin from Table To simulate quantitative traits unrelated to these genetic variants, we drew phenotype values from a Normal , i.e., within each MAF, Rsq, and effect size bin, we calculated power as the average number of times that p-value < 5 × 10–8.Quantitative traits for the APOC3.We considered 56,073 unrelated samples of white European ancestry from the UK Biobank with measured TG levels and genetic data. As in Auer et al. , we regrAdditional file 1.


Pennisetum glaucum [L.] R. Br.) is a nutrient-dense, relatively drought-tolerant cereal crop cultivated in dry regions worldwide. The crop is under-researched, and its grain yield is low (< 0.8 tons ha−1) and stagnant in the major production regions, including Burkina Faso. The low productivity of pearl millet is mainly attributable to a lack of improved varieties, Striga hermonthica [Sh] infestation, downy mildew infection, and recurrent heat and drought stress. Developing high-yielding and Striga-resistant pearl millet varieties that satisfy the farmers’ and market needs requires the identification of yield-promoting genes linked to economic traits to facilitate marker-assisted selection and gene pyramiding. The objective of this study was to undertake genome-wide association analyses of agronomic traits and Sh resistance among 150 pearl millet genotypes to identify genetic markers for marker-assisted breeding and trait introgression. The pearl millet genotypes were phenotyped in Sh hotspot fields and screen house conditions. Twenty-nine million single nucleotide polymorphisms (SNPs) initially generated from 345 pearl millet genotypes were filtered, and 256 K SNPs were selected and used in the present study. Phenotypic data were collected on days to flowering, plant height, number of tillers, panicle length, panicle weight, thousand-grain weight, grain weight, number of emerged Striga and area under the Striga number progress curve (ASNPC). Agronomic and Sh parameters were subjected to combined analysis of variance, while genome-wide association analysis was performed on phenotypic and SNPs data. Significant differences (P < 0.001) were detected among the assessed pearl millet genotypes for Sh parameters and agronomic traits. Further, there were significant genotype by Sh interaction for the number of Sh and ASNPC. Twenty-eight SNPs were significantly associated with a low number of emerged Sh located on chromosomes 1, 2, 3, 4, 6, and 7. Four SNPs were associated with days-to-50%-flowering on chromosomes 3, 5, 6, and 7, while five were associated with panicle length on chromosomes 2, 3, and 4. Seven SNPs were linked to thousand-grain weight on chromosomes 2, 3, and 6. The putative SNP markers associated with a low number of emerged Sh and agronomic traits in the assessed genotypes are valuable genomic resources for accelerated breeding and variety deployment of pearl millet with Sh resistance and farmer- and market-preferred agronomic traits.Pearl millet ( Pennisetum glaucum [L.] R. Br., 2n = 2x = 14) is a highly nutritious and a key staple food crop in dry regions worldwide. It is the major crop of the Sahel region, including Burkina Faso1. In Africa, pearl millet is cultivated on an estimated area of 13.8 million hectares (ha), with an average yield of 0.7 tons ha−12. In Burkina Faso, an estimated area of 1.2 million ha is devoted to pearl millet production. However, the mean yield of the crop in the country is low (< 0.81 tons ha−1), lesser than the global average of 0.9 tons ha-12. The low grain yield in the farmers’ fields is attributable to various biotic and abiotic constraints, including the use of low-yielding landraces, Striga hermonthica (Sh) infestation, bird damage, insect pests, diseases, heat and drought stresses4.Pearl millet (Striga hermonthica (Del.) Benth. is the most significant biotic constraint to pearl millet production and productivity in Burkina Faso and yield losses vary between 40 and 55%6. The parasite infests several other major cereal crops, including rice (Oryza glaberrima Steudel and O. Sativa L.), maize (Zea mays L.), sorghum (Sorghum bicolour [L.] Moench), and fonio (Digitaria exilis [Kippist] Stapf)8. Farmers often abandon Sh-infested fields and switch from pearl millet to other non-host crops, reducing the crop’s overall production and economic value9. Striga is a highly prolific parasite in Burkina Faso attributed to the host crop being mostly grown in semi-arid parts of the Sahelian and Sudano-Sahelian zones, which are dominated by poor soil fertility, low and erratic rainfall, and high temperatures that favour germination, growth and spread of the weed3.Striga control is difficult because each parasitic plant can quickly disperse and deposit thousands of seeds into the soil seedbank. Furthermore, Striga seeds can remain viable in the soils for more than 14 years10. Striga hermonthica is a major threat to food security, exacerbating hunger and poverty in many African countries12. Monetary losses ranging from 117 to 200 billion US$ is incurred annually due to crop damage, and increases by US $30 million annually13.Striga control strategies are recommended, including hand weeding, mulching crop fields with biomass of the shea tree (Vitellaria paradoxa C.F. Gaertn.) as a bio-control agent, optimal fertilizer application, and soil moisture management14. These strategies improve the soil properties, promote crop growth and development, and retard germination and growth of Striga15. Herbicides are less effective in controlling the effect of the parasite after emergence, and they are unaffordable for smallholder farmers.Several Striga-resistant pearl millet varieties is the most sustainable and environment-friendly management option for smallholder farmers in semi-arid regions. Resistant cultivars support fewer Striga plants and yield higher17. However, with the paucity of locally adapted and Sh-resistant donor sources, breeding for Striga resistance in pearl millet is still challenging compared to other cereals21. In the past decade, intensive research on the interaction of Striga with the host at the molecular level has opened opportunities to develop new management strategies22. For instance, 154 candidate genes associated with Sh resistance traits were identified in maize23. Adewale et al.24 reported 13 associated markers with the Sh resistance trait in early maturing tropical white maize inbred lines.The use of 25, iron, zinc and protein content26, downy mildew resistance27 and Sh resistance28. Also, GWAS has been used in finger millet for the identification of genes associated with Striga resistance29 and grain nutritional contents30 and for genetic diversity analysis31. GWAS is a valuable genomic tool to identify quantitative trait loci (QTLs) linked to Striga resistance for marker-assisted selection. GWAS results depend on the genetic marker and its density, genetic composition and diversity of the test populations.Genome-wide association studies (GWAS) has been used in pearl millet for the identification of putative genes related to flowering time9. Genetic markers such as GRMZM2G077208, GRMZM2G164502, GRMZM2G018508, and GRMZM2G171986, located on chromosomes 3, 5, 7, and 9 were reportedly significantly associated with Sh count in tropical maize germplasm32. SNP markers are instrumental in the dissection of complex traits such as Striga resistance, and their association with the trait can be revealed through GWAS. Identification of genomic regions linked to Striga resistance in pearl millet breeding would speed up the development of Striga-resistant varieties. Genetic markers improve the efficiency of novel Striga-resistant genes introgression and pyramiding into high-yielding elite varieties through backcross method. Therefore, the objective of this study was to undertake genome-wide association analyses of agronomic traits and Sh resistance among 150 pearl millet genotypes to identify genetic markers for marker-assisted breeding and trait introgression.Genetic markers are landmarks on chromosomes that help pinpoint the location of genes of interest. They can be detected through morphological and molecular markersP < 0.001) for days to 50% flowering (DTF), plant height (PH), number of tillers per plant (NT), panicle length (PCL), panicle weight (PWT), thousand-grain weight (TGW), and grain weight (GW) under Sh infestation. Genotypes differed significantly (P < 0.001) for the area under the Striga number progress curve (ASNPC). The genotype by Striga interaction was non-significant for the NT and ASNPC. The genotype by environment interaction differed significantly (P < 0.001) for days to 50% flowering (DTF), plant height (PH), number of tillers per plant (NT), panicle length (PCL), panicle weight (PWT), thousand-grain weight (TGW), and grain weight (GW) under Sh infestation  and SN2 (28.61%), while high heritability values ranging from 75.07 to 92.42% were recorded for the DTF, PCL, TGW, and PH  associated with Sh resistance located on chromosomes 1, 2, 3, 4, 6, and 7. Two SNPs, S3_113184999 and S3_113184999, were located on the same position and were associated with the number of Sh counted  SNP markers, S1_75620319, S3_1159738, and S6_231436300, were associated with Striga resistance in the naturally Striga-infested field on chromosomes 1, 3, and 6. In the greenhouse, 10 significant SNPs were associated with Sh-resistance, while 15 SNPs were associated with Sh-resistance on chromosomes 1, 2, 3, 4, 6, and 7 in a pooled analysis.Twenty-eight SNP markers were significantly  for agronomic traits ranged from 75.07 to 92.42% and 18.17 to 28.61% for emerged Sh count (Table 39. Traits with high heritability are easy to select and improve using marker-assisted selections and pyramiding in a desirable genetic background. The lower broad-sense heritability estimates of 18.17 and 28.61% for the number of emerged Striga (SN1 and SN2)  in the offseason of 2020/21. The Didri site is located at 12° 12′ 15" N and 1° 14′ 13" W and is a hotspot site for Sh affecting pearl millet, maize and sorghum crops. The site received an annual rainfall of 748.5 mm for 46 days during the 2019/20 rainy season and has sandy soils. The INERA site is located at 12°28/27 N and 1°33/31W.A field experiment was conducted in the 2019/20 main growing season in a naturally infested The study used 148 pearl millet genotypes collected from the International Crop Research Institute for the Semi-arid Tropics (ICRISAT) in Niger and two elite breeding lines from INERA/Burkina Faso. The descriptions of the test genotypes are summarised in Table Sh seed collected from farmers' fields in Burkina Faso. Pearl millet seeds in the naturally Striga-infested field (hereafter designated as GenInf), were sown during the main crop growing season from June to October 2019. Genotypes were established in 4.2 m long rows spaced at an inter-row spacing of 160 cm and intra-row spacing of 60 cm, providing a total plot size of 6.72 m2 per genotype. Four seeds were initially sown per hill and later thinned to one plant two weeks after planting. A total of three plants was selected randomly from the middle of the experimental unit and tagged for agronomic data collection. In the greenhouse, one healthy and vigorous plant was grown per pot for the test genotypes with Sh (hereafter denoted as GenStr), and genotypes without Sh (hereafter referred to as control) treatments. Standard agronomic practices recommended for pearl millet production were followed. Experimental units were fertilized using nitrogen, phosphorus and potassium (NPK: 14:23:14) and applied as a microdose of 3 g per hill 15 days after planting. Hand weeding was routinely done after the first hoeing to remove all other weeds except Striga.The field and greenhouse experiments were laid out using a 10 × 15 alpha lattice design with two replicates. In the greenhouse, 5L plastic pots were used and filled with a soil medium composed of clay, sand, and organic manure at a ratio of 2:1:1 respectively. Two weeks before planting, each pot was infested with a scoop of sand mixed with 0.05 g of 1-year-old The following agronomic parameters were collected from pearl millet: days-to-50%-flowering (DTF) were recorded as the days when 50% of the plants in each plot had intruded stigma. Plant height (PH) was measured in cm from the base to the top of the panicle of the main tiller. The number of tillers per plant (NT) was recorded by counting the number of tillers with panicles for the tagged plants. Panicle length (PCL) was measured in cm from the base to the top of the main tiller panicle. Panicle weight (PWT) was recorded in grams by weighing the harvested panicles for each entry after 14 days of sun-dry, and thousand-grain weight (TGW) was determined in grams by weighing one thousand-grain for each of the entries. Grain weight per plant (GW) was determined in grams by weighing the grain after threshing and dividing it by the number of harvested plants for each plot.Striga parameters, the number of emerged Sh plants per plot were recorded at 70 and 96 days after sowing in the naturally infested field for each row, excluding the borders. The number of emerged Sh plants were counted per pot 116 and 144 days after sowing in the greenhouse. The area under the Striga Number Progress Curve (ASNPC) was computed using the successive Striga counts according to Haussmann et al.48 as follows:Striga assessment dates, Yi is the Striga count at the ith assessment date, Y(i+1) is the Striga count at the ith plus 1 assessment date, ti is the number of days after planting (DAP) at the ith assessment date, t(i+1) is DAP at the ith plus 1 assessment date.For Striga data were subjected to analysis of variance using GenStat 19th Edition (http://www.genstat.co.uk). Homogeneity of variance test was done for each site using the Bartlett49 procedure before combined analyses. The treatment, genotype, and genotype × treatment interaction significance tests were computed using GenStat. The Best Linear Unbiased Prediction (BLUP) was calculated according to Haslett and Puntanen50 to predict the accuracy and to aid selection. The area under the Striga number progress curve was drawn using R. ASReml-R Version 4 was used to fit the linear mixed models using Residual Maximum Likelihood (REML) in R51.Both the crop and https://doi.org/10.1038/nbt.3943). A raw marker set consisting of 29 million SNPs generated from 345 pearl millet genotypes from which 148 genotypes used in the study was sourced from the Nature Paper Pearl Millet (https://doi.org/10.1038/nbt.3943) and filtered using Tassel v4.2 for site coverage of 90%, minor allele frequency of 0.1, taxa coverage of 30% and maximum heterozygosity of 50%. The resulting final set of variants contained 256 K SNPs was used for the current analysis. Phenotypic data collected from 150 genotypes were available for marker-trait association analysis. Principal component analysis (PCA) was calculated, and the resulting eigenvalue (7) was used for genome-wide association analysis (GWAS) following multiple methods procedures generated from GAPIT v3.052. The Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) software was used to determine the significant variations among pearl millet and Sh traits owing to its ability to produce fewer false positives and more true positives than the GWAS method, FarmCPU42. Liu et al.43 reported the power of BLINK to outperform FarmCPU relative to statistical capabilities vs False Discovery Rate (FDR) and statistical power vs type I error.To assemble the pearl millet genome, whole genome shotgun (WGS) and bacterial artificial chromosome (BAC) sequencing were used. Ten small inserts  and 13 large inserts  WGS libraries were constructed using Tift 23D2B1-P1-P510 genotype. These libraries were sequenced on the Illumina HiSeq 2000, and 520 Gb of sequence data, representing 296 × genome coverage. Two BAC libraries, with an average insert size of ~ 120 kb, were constructed from Tift 23D2B1-P1-P5 using EcoRI and HindIII. Nine hundred seventy-two Gb of sequence data were generated from 100,608 BAC clones at ~ 80 × genome coverage. In brief, 1.49 Tb of sequence data, after stringent filtering and correction steps, were assembled into 1.58 Gb of contigs and 1.82 Gb of scaffolds (Sh resistance and agronomic traits through GWAS involving 150 pearl millet genotypes in Burkina Faso. There were significant genotypes by Sh interaction for assessed agronomic traits, the number of Sh and ASNPC. Twenty-eight SNPs were associated with Sh traits on chromosomes 1, 2, 3, 4, 6, and 7. SNPs markers associated with DTF, PCL, and TGW were located on chromosomes 2, 5, 6, and 7; chromosomes 2, 3, and 4; and chromosome 6, respectively. After successful validation, the new markers would be deployed for marker-assisted breeding emphasising the above agronomic traits and Sh resistance in pearl millet in Burkina Faso or related agro-ecologies.The current study detected significant genetic variability and markers for


Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations  across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments.Genome-wide association studies were conducted using a globally diverse safflower (The online version contains supplementary material available at 10.1007/s11032-022-01295-8. Carthamus tinctorius L.) is a member of the Compositae family, grown as a vegetable, cut flower, herbal medicine, animal feed, birdseed, and oilseed, etc. in over 60 geographical regions covering the Middle East, Africa, America, Europe, and Asia  in safflower has been sequenced with genes being isolated and cloned , which is also referred to as the traits phenotypic plasticity Bradshaw . IdentifIn Australia, crop production is challenged by spatial drought patterns due to seasonal rainfall and high temperatures , including elite cultivars, breeding lines, and landraces. Accessions information and the field trial experiment design are previously described  and standard error of prediction (SEP) were 0.93 and 0.7% for seed protein content and 0.95 and 1.2% for seed total oil content.Days to flowering (DF) was recorded as the number of days from sowing to 25% of the plot flowering. Plant height (PH) was measured at the late flowering stage from the ground surface to the top of the plot canopy in cm. Seed weight (SW) was measured as random 500 achenes from the whole plot in grams. Grain yield was measured as yield per plot (YP) in kilograms harvested by machine. Seed protein (PR) and seed oil content (OL) were determined by near-infrared reflectance spectroscopy  with calibration by the Dumas nitrogen combustion method for protein , and the Soxhlet extraction for oil . The NIR prediction models R-squared  for each trait at each site were calculated by a single site linear mixed model with safflower accessions fitted as fixed effects. The model was illustrated as:Ymijk is the phenotypes of accessions m in rep j at row i, column k; µ is the overall mean, gm is the fixed accession genetic effect, and Rj is the replicate effect; ri is the row effect, ck is the column effect, and ɛmijk is the residual, including the AR1 × AR1 covariance structure to adjust spatial variation.where Pearson’s correlation at each site was calculated based on the BLUEs of each trait. BLUEs were used as the “phenotypes” for the GWAS.To assess the G × E level for each trait, the four sites were combined, and the genetic effect associated with accessions was decomposed into two components, the genetic effect of accessions and the interaction effect between accessions and sites (G × E effect), which were assumed to be homogenous for all sites. The linear mixed model was:Yijk is the phenotype of accession k in rep j at site i, µ is the overall mean, Si is the fixed i-th site effect, Rj is the fixed replicate effect, Gk is the random accession genetic effect, SGik is the random G × E effect, and ɛijk is the residual. Two models, including and excluding the G × E effect, were compared, and a log-likelihood ratio test was used to test the significance of the G × E effect for each trait  was estimated as the ratio of the genetic effect of accessions to total genetic variance, calculated as rGoverall = where A heterogeneous variance structure was also fitted in the linear mixed model, which assumes that accessions genetic effect is independent at each site. It can be illustrated as:rGij = i and j. Similar to the above, high genetic correlations between two sites indicated a low G × E interaction. The significance of the genetic correlation between two sites was tested for deviation from 1 using likelihood ratio tests. If rGij significantly differed from 1, it suggested the ranking of accessions at the two sites was different. Akaike information criterion (AIC) was used to compare the fitness of models 2 and 3.where the terms are the same as above, with the site as a fixed effect and the accession and trial replicate effects both nested within sites as a random effect with different variance for each site. The residual variance was also nested within the site, with the AR1 × AR1 covariance structure used to adjust spatial variation across columns and rows. The genetic correlation of the accession effect between two sites was calculated as A total of 349 accessions were genotyped using a genotyping-by-sequencing assay as described in , plant height (PH), days to flowering (DF), 500 seed weight (SW), seed protein (PR), and oil content (OL) are shown in Fig. Pearson correlations between traits at each site showed that YP was positively correlated with PH (0.34–0.479) and negatively correlated with PR (− 0.208– − 0.405). SW was negatively correlated with OL (− 0.518– − 0.505), PR (− 0.55– − 0.383), and PR was positively correlated with OL (0.19–0.639) over all sites. However, DF is positively correlated with PH at sites 1, 3, and 4 (0.464–0.62) and negatively correlated with PH at site 2  among sites were observed for SW and OL , indicating low G × E interaction for those traits, while low rGoverall for YP (0.48) indicated a strong G × E interaction. G × E levels were moderate for PH, DF, and PR, with rGoverall value ranging from 0.67 to ~ 0.79 . The model including G × E interaction effects had a higher log-likelihood than the model excluding G × E interactions, and the G × E interaction effects were all significant (Supplementary Table rGij) between pairwise site combinations varied; however, traits with high rGoverall also had high rGij between pairwise sites  was not significantly different from 1. As the rest traits all had rGij ≥ 0.80 between sites 3 and 4, suggesting that those two sites could be treated as a single site with limited re-ranking. Genetic correlations for PH were uniformly high between pairwise sites (rGij ≥ 0.80), while the genetic correlations for DF and YP varied, with those for YP being the most variable.To account for differences in G × E interactions between sites, a linear mixed model assuming heterogeneous genetic and residual variances (model 3) was adopted. The AIC was lower for all traits for model 3 than for model 2, suggesting that model 3 fitted the data better  revealed a strong population structure among 318 accessions , respectively. Locus 28,935 was associated with low SW and high OL at sites 2 and 3 (or site 4) . Our results showed that G × E patterns differed between safflower traits. The identification of site-shared and site-specific MTAs in GWAS provides knowledge to broaden our understanding of the genetic basis of G × E interactions for important safflower traits.r = 0.476)  had an impact on safflower agronomic traits. Safflower is normally sown in Winter in Australia to maximize the usage of the available water from Winter and early Spring rain  Oz , indicat.476) Oz . The neg.476) Oz .rGoverall was observed for DF and PH, the pairwise genetic correlation showed that lines were reranked strongly for DF at site 2 compared with other sites. The genetic divergence of DF among the accessions in response to water stress at flowering implied DF is important in developing drought tolerant varieties  by all three GWAS approaches, of which 18 were shared across sites, and 12 were site-specific, indicating the complex genetic control of this trait. Studies with canola showed that 24 candidate genes were involved in fatty acid biosynthesis , days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and seed protein content (PR) in a globally diverse safflower collection grown in four field trials. The heterogenous mixed linear model (MLM) fitted data better and provided a detailed estimation of the G × E pattern. We observed that different water stress conditions impacted the performance of each of these traits differently, with low overall G × E observed for OL and SW and high overall G × E for YP. In total, 92 MTAs were identified with large effects MTAs detected for OL, SW, and PR across all sites. Site-specific MTAs were detected for all traits with differed allelic effects, suggesting these MTAs could be associated with trait G × E. Five MTAs were associated with multiple traits. The uniform GWAS thresholds used in the study could have impacted the number of significant SNP identified for complex traits. This study has provided new insights into the genetic architecture of the traits studied, and it presents opportunities to exploit the MTA identified in breeding programs to increase yield stability and local adaptation in safflower.Supplementary file1 (DOCX 973 KB)Supplementary file2 (XLSX 121 KB)Below is the link to the electronic supplementary material.


Genetic prediction of common complex disease risk is an essential component of precision medicine. Currently, genome-wide association studies (GWASs) are mostly composed of European-ancestry samples and resulting polygenic scores (PGSs) have been shown to poorly transfer to other ancestries partly due to heterogeneity of allelic effects between populations. Fixed-effects (FETA) and random-effects (RETA) trans-ancestry meta-analyses do not model such ancestry-related heterogeneity, while ancestry-specific (AS) scores may suffer from low power due to low sample sizes. In contrast, trans-ancestry meta-regression (TAMR) builds ancestry-aware PGS that account for more complex trans-ancestry architectures. Here, we examine the predictive performance of these four PGSs under multiple genetic architectures and ancestry configurations. We show that the predictive performance of FETA and RETA is strongly affected by cross-ancestry genetic heterogeneity, while AS PGS performance decreases in under-represented target populations. TAMR PGS is also impacted by heterogeneity but maintains good prediction performance in most situations, especially in ancestry-diverse scenarios. In simulations of human complex traits, TAMR scores currently explain 25% more phenotypic variance than AS in triglyceride levels and 33% more phenotypic variance than FETA in type 2 diabetes in most non-European populations. Importantly, a high proportion of non-European-ancestry individuals is needed to reach prediction levels that are comparable in those populations to the one observed in European-ancestry studies. Our results highlight the need to rebalance the ancestral composition of GWAS to enable accurate prediction in non-European-ancestry groups, and demonstrate the relevance of meta-regression approaches for compensating some of the current population biases in GWAS. We investigate the performance of polygenic scores (PGSs) in predicting complex traits in non-European populations. We show through simulations that the genetic heterogeneity of a trait is a key factor influencing PGS predictions, and we highlight the need to rebalance the ancestral composition of future genetic studies. We use R-squared values to assess the predictive performance of the PGS under a direct model where we consider that individual data are available or under an indirect model relying on summary statistics. We report results in each of the five 1000 Genomes ancestries by averaging the measures of fit across the corresponding subpopulations.We simulate data under a wide range of scenarios following the procedure represented in Overall, varying the heritability or the number of causal SNPs in the simulations affects the absolute prediction performance of the three PGSs but does not impact the relative performance between them  and S2. Our comparisons show that AS and FETA/RETA PGS are the most strongly impacted by the sample size and the ancestry-related genetic heterogeneity, respectively. We next perform simulations under real-world scenarios for two complex traits that vary in heterogeneity, heritability and polygenicity, and have been recently investigated in large trans-ancestry studies: T2DOverall, genetic heterogeneity and its relationship with ancestry composition of the discovery sample have the greatest impact on the relative accuracy of the methods. This was observed both on theoretical scenarios and on scenarios approaching real human complex traits, T2D and TGs. Our results highlight the advantage of meta-analysis approaches that model ancestry-related genetic heterogeneity, such as meta-regression, which offers optimal accuracy levels. Nevertheless, even this method benefits from the inclusion of non-EUR individuals, especially if there is genetic heterogeneity underlying the trait of interest, emphasizing the need to include more diverse samples in future studies.Our simulations were based on several simplifying assumptions to reduce computational burden. First, our simulations are based on independent variants to mimic a classical approach where PGS is built upon variants obtained from clumping and selection based on the p value of association.,,,In summary, our results from simulations both on hypothetical and real trait scenarios show that PGS accuracy highly depends on the underlying genetic architecture of the trait, especially heterogeneity among populations. In traits with higher levels of genetic heterogeneity, TAMR PGS offers advantages compared with a fixed-effects meta-analysis or an AS score but the performances are still low given the current ancestry distribution. Predictions in non-European populations in this setting will also most probably benefit from the recent developments in trans-ancestry PGS given the results obtained with CT-SLEB in T2D scenarios. Our simulation results are in line with other studies highlighting the poor transferability of PGS to non-EUR populations


While heritability estimates were less impacted by weighting , we found substantial discrepancies for genetic correlations  and Mendelian randomization estimates  for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses ( The authors use information on 14 traits and create a representative pseudo-sample of the UK Biobank population, showing that participation bias distorts behavioural genome-wide association study and Mendelian randomization findings. To maximize power for genome-wide discovery, researchers curate large biobanks with rich genetic and phenotypic data. To ensure the validity of findings in genome-wide association (GWA) studies, researchers aim to eliminate potential sources of bias, such as population stratification, assortative mating, measurement error and indirect genetic effects7. Under certain conditions, research on non-representative samples can lead to valid conclusions—for example, when study participation is unrelated to both the independent and dependent variables. However, many commonly studied factors influence study participation. These may include mental and physical health, substance use , income, and educational attainment12, where study participants are typically healthier than the target population. Such ‘healthy-volunteer bias’ is well documented in the UK Biobank (UKBB), one of the most widely used resources for biomedical research. Of the nine million people invited to take part in the UKBB, only 5.5%  participated in the study—a sample of volunteers with healthier lifestyles, higher levels of education and better health than the general UK population14.A particularly challenging bias that is typically not considered in genetic studies can occur when biobanks collect data from individuals that are not representative of their target population15. If study participation includes a genetic component, biased estimates are also expected in genetic studies16. In gene-discovery studies, non-random participation may distort the association between a genetic variant and the outcome , the extent to which participation bias affects gene discovery and downstream analyses is currently unknown.Given the growing reliance on non-representative biobanks, it is paramount to assess the extent to which study participation induces bias in genome-wide studies and downstream analyses. In observational studies using UKBB data, participation bias has already been shown to distort phenotypic exposure–outcome associationsome Fig. . In Mendome Fig. . Recent Researchers can correct for participation bias by the use of samples that are representative of their target population—a broader group from which a study sample is drawn and to which the study results should generalize. In case of the UKBB, the target population is middle-aged to older adults of recent European ancestry living in the United Kingdom, which is not the same as the general UK population . Here wen = 81,118, we retained n = 22,646 after applying the same inclusion criteria used for UKBB recruitment  shows that the profile of the HSE sample closely matches that of the Census sample (Supplementary Table P) of female gender , proportion of individuals of age ≥65 , mean (M) age when individuals completed full-time education  and proportion of retired individuals . Further inspection of the associations between variables available in the HSE and UK Census  cohorts comprising a total sample of ruitment . After fn = 2463), individuals from Scotland or Wales , individuals who self-identify as non-white  and individuals withdrawing consent (n = 161). We further removed 21,868 (5.27%) individuals with missing data for any of the auxiliary variables. Since these individuals can be considered a special case of missingness due to non-participation, which the probability weights were designed to compensate for, we did not impute missing data for the auxiliary variables. The sampling weights were generated for n = 393,299 UKBB individuals, of which 109,550 were removed after we applied quality control steps for genome-wide analyses , we excluded individuals of age >69 and <40  for UKBB individuals. We obtained the probabilities used to construct the weights from a LASSO regression model retaining 454 of the 903 initially included predictors. Figure We derived a model for participation probability by comparing 14 harmonized characteristics of UKBB participants with those of a representative sample (HSE). The application of the resulting probability weights then facilitates the creation of a (weighted) pseudo-sample of the UKBB that is more representative of its (representative) target population (HSE). Figure win) and HSE participants were given a weight of 1. When we applied probability weighting  population distributions. We included the generated weights in a univariate logistic regression model predicting UKBB participation, where UKBB individuals were given their normalized weight (rUKBB) and the reference sample (rHSE). The largest differences (rdiff = |rHSE − rUKBB|) were for employment status with overall health , overall health with age , household size with income  and employment status with income   Fig. . The appFinally, Fig. In summary, using probability weighting, we created a pseudo-sample of the UKBB population achieving higher levels of representativeness along the 14 variables used in the weighting model. As a consequence, the weighting also changed the distributions of some variables not used in the weighting model . Probability weighting thus provides a useful tool for examining bias due to selective participation in genomic studies, by evaluating how reweighting affects genome-wide results and downstream analyses.neffective = 94,643 to 102,215, depending on the trait) and standard GWA analyses . Reductions in the effective sample size in wGWA result from variability among the probability weights: when the weights are normalized to have a mean of one, the effective sample size simplifies to n × {1/[Var(win) + 1]}. This quantity thus depends on the unweighted study sample size and on the variance of the normalized weights across study participants (win).We next studied how the results of GWA analyses differ between weighted GWA (wGWA) (P < 5 × 10−8), effect size reduction following weighting was more common  than increase . More specifically, effect size increase was the most common for cancer (57% of SNPs), loneliness (50%), education (33%) and reaction time (33%), whereas reduction was present for depression/anxiety (67%), coffee intake (63%) and smoking status (58% of SNPs). While a shift towards more representativeness led to both effect size increases and decreases, we found no evidence of changes in the direction of effects , 25 SNPs (1.48%) reached significance only in the weighted analyses. We found new SNPs for 12 of the 19 included traits, most notably for depression and anxiety (50% new genome-wide SNPs), cancer (29%) and loneliness (25%). The detailed results are listed in Supplementary Table Second, with respect to genome-wide discovery Fig. , we founUKBB = 54.38%) than its target population . Probability weighting recovered the target population prevalence in the UKBB . SNP heritability estimates (h2) . Supplementary Fig. P < 5 × 10−8, in an independent sample of >2,400,000 volunteers) to estimates obtained from standard GWA and wGWA. Of those, 18 SNPs (36.73%) showed significantly lower sex-associated effects in wGWA. In contrast, only 3 SNPs (6.12%) had significantly lower sex-associated effects in standard GWA.The UKBB included proportionally more women , of which we selected 23 linkage disequilibrium (LD)-independent SNPs after clumping. Supplementary figures (Manhattan and QQ plots) and information (gene and phenotype annotation) for these SNPs are available in Supplementary Figs. We conducted a wGWA on UKBB participation in h2 = 0.009 . LD-score regression analyses , income (rg = 0.77), participation  , intelligence (rg = 0.62) and cigarette use (age of onset) (rg = −0.70).SNP heritability for UKBB participation was ses Fig.  implicatrg,DIFF = rg − rg,w) between standard GWA and wGWA analyses , five showed significant (PFDR < 0.05) We next assessed differences in SNP heritability ses Fig. . On averion n = 1, five shrg,DIFF| = 0.07  between results obtained from standard GWA and wGWA analyses. rg decreased the most notably for rg , rg  and rg . The increase in rg after weighting was the most prominent for rg , rg  and rg . For five (3.27%) of the assessed trait pairs (n = 153) the weighted and standard genetic correlations were significantly (PFDR < 0.05) different, of which education was the most implicated trait , Supplementary Section Concerning estimates of genetic correlations, we found an average difference of |Figure k = 234), 14 (6%) estimates were either decreased  differential effects for two exposure–outcome associations (education on BMI and smoking status on fruit consumption). There was little evidence of changes in the direction of MR estimates as a result of weighting, which occurred for only two exposure–outcome pairs, neither of which was significant (αDIFFPFDR > 0.05) . Associations between lifestyle choices, including coffee intake on BMI  and conducted inverse-probability-weighted GWA analyses on 19 traits. Conducting genome-wide analyses in a more representative (weighted) sample of the UKBB, we found that selective participation can distort genome-wide findings and downstream analyses.Overall, increasing representativeness mostly affected the magnitude of effects rather than their direction. We found several differences in estimates in all sets of genome-wide analyses, in both directions . Of note, although effect size estimates can increase with the use of more representative samples, the increased standard errors of the inverse probability weighting (due to reduced effective sample size) make new SNP discovery difficult. Despite this caveat, using wGWA revealed new loci for 12 traits. Reweighting also changed heritability estimates, genetic correlations and MR estimates, most notably for socio-behavioural traits including education, diet, smoking and BMI.24, as well as our findings of high genetic correlations between the liability to UKBB participation and socio-behavioural traits . More broadly, different sources of bias probably affect similar phenotypes in genome-wide studies, in that genome-wide findings on socio-behavioural phenotypes are biased by selective participation24, indirect genetic effects3, assortative mating4, error in measurements25 and population stratification26.In contrast, we observed smaller changes between wGWA and GWA estimates for molecular and physical traits . This pattern is in line with existing studies15: participation bias, defined as the difference in exposure–outcome associations in the UKBB and the reference sample (HSE), was substantial for several associations. For example, phenotypically, participation bias distorted the association of overall health with age and employment status. The application of probability weighting eliminated a significant proportion (>90%) of bias due to selective participation in the UKBB.Our work builds on and extends recent efforts evaluating bias due to selective participation. We replicate findings showing that phenotypic exposure–outcome associations in the UKBB differ from those estimated in probability samplesWe highlight patterns of bias and point to areas of research that are the most impacted by this bias. Since GWA summary statistics are increasingly used in epidemiological research to study causal questions concerning education, diet and behaviour, greater care should be taken when relying on data obtained from non-random samples. If researchers cannot assess participation bias in biobank data , their data may be of only limited use when scrutinizing genotype–phenotype relationships. As part of this work, we provide software to perform wGWA, which allows researchers to conduct sensitivity checks when relying on non-representative samples. Alternatively, recruitment schemes incorporating probability sampling can help reduce bias, but samples are typically small given the substantial costs associated with recruitment.Our results should be interpreted with caution. First, while the application of probability weighting successfully reduced bias resulting from selective participation in the UKBB based on our 14 variables, residual bias still exists. We may have missed important factors independently predicting UKBB participation when modelling participation probability, as we chose our auxiliary variables on the basis of the availability of variables that could be harmonized between the UKBB and the reference sample. Still, some of these omitted variables may be proxied by (the combination of) some of the 14 variables, hence not compromising the probability weights. Probability weighing would not correct bias in situations where the exposure and the outcome of interest both link to an aspect of study participation that is unrelated to the auxiliary variables. This also means that wGWA for outcome traits such as education level is expected to be accurate, since this trait has been used when modelling participation probability. Finally, even for outcome traits completely unrelated to the 14 auxiliary variables but linked to traits influencing study participation, it is extremely unlikely that wGWA would be more biased than unweighted GWA. Hence, when substantial differences are observed between wGWA and standard GWA results, it is likely that the latter is (more) biased. Still, weighting—like any other method of adjusting for non-representativeness—should therefore be considered as only the second-best option when tackling participation bias, as only the implementation of probability sampling at the recruitment stage can ensure full elimination of this type of bias.Second, when choosing a reference population, there is a trade-off between the representativeness of the reference sample and the number of available variables to match the samples. We chose to use the HSE as a reference sample to strike a balance between these two factors, but biases can remain if the reference sample is not representative enough. Third, genome-wide analyses were restricted to phenotypes with little missing data. This is a shortcoming since traits with substantial missing data are perfect candidates for characteristics influencing participation. We therefore did not evaluate the impact of participation bias on variables collected at follow-up.27. Similarly, the genome-wide results discussed here can be generalized only to adults of European genetic ancestry who also self-identify as white. Future work should also assess the impact of participation bias in more diverse samples, notably other ancestries and racial and ethnic groups, as well as younger individuals.Finally, the UKBB probability weights are sample-specific, constructed for a sample that is better educated, healthier and older and includes more women than the target population. Bias due to selective participation will differ across study contexts, and the participation mechanisms evaluated in this study are therefore not generalizable to other cohorts. For example, large health-registry-based biobanks, where older individuals with poorer health tend to be over-represented, do not have the healthy-volunteer bias but have different kinds of selection biasesIn conclusion, our results highlight that GWA and downstream analyses are sensitive to bias resulting from selective participation, most visibly for socio-behavioural traits. Moving forward, more efforts ensuring either sample representativeness or methods correcting for participation bias are paramount, especially when studying the genetic underpinnings of behaviour, lifestyles and educational outcomes.We first derived a model for participation probability by comparing 14 harmonized characteristics of the UKBB sample with those of a representative sample. Using the estimated participation probabilities, we conducted wGWA analyses on 19 UKBB traits. Second, to explore the genetic basis of UKBB participation, we conducted a GWA on the participation probability and evaluated the genetic findings. Finally, comparing wGWA results with those obtained from standard GWA analyses, we assessed the impact of participation bias on the estimation of three frequently studied quantities: (1) the effect of genetic markers on complex traits, (2) heritability and genetic correlation estimates, and (3) exposure–outcome associations obtained from MR.29. Included in this study were data from UKBB participants of European genetic ancestry who also identify as white and passed standard GWA analysis quality control measures30. We further filtered the sample according to geographic region  to match the geographic regions included in the reference sample (HSE), and we removed individuals with missing data in the auxiliary variables used to generate the propensity scores (further described below). The UKBB resource was approved by the UKBB Research Ethics Committee, and all participants provided written informed consent to participate.The UKBB is a large-scale prospective population-based research resource focusing on the role of genetic, environmental and lifestyle factors in health outcomes in middle age and later life. More than 9,000,000 men and women between 40 and 69 registered with the UK National Health Service were invited to take part. Of those, 5.4%  were recruited in 22 assessment centres across England, Wales and Scotland between 2006 and 201031. In our study, we included data from five cohorts recruiting a sample of more than 80,000 individuals between 2006 and 2010 . We applied the same inclusion criteria to the HSE data as used for UKBB recruitment, retaining only individuals aged between 40 and 69 years who self-identify as white. HSE response rates ranged between 64% and 68%31. HSE sample weights are supplied to account for the unequal probabilities of selection and non-response32, weighing individuals as a function of sex, household type, region and social class. In this study, the HSE weights were incorporated in LASSO regression predicting UKBB participation (described below).The HSE is an annual probability sample set out to measure health and related behaviours in a nationally representative sample of adults and children living in private households in England33, which runs every ten years to collect basic demographic variables  through a paper-based or online questionnaire. With a 95% response rate, the UK Census Microdata is highly representative of the UK population. We applied the same selection criteria to the Census data as to the UKBB and HSE , resulting in a relevant sample of n = 895,649. We extracted all variables that could be harmonized with the UKBB and HSE data  and a representative reference sample. In this study, we selected auxiliary variables tapping into dimensions related to health, lifestyle, education and basic demographics. We included all variables that could be harmonized across the two datasets (HSE and UKBB) with few missing observations . Fourteen variables derived from 12 measures were included and harmonized across the two datasets. The five continuous variables included age, BMI, weight, height and education . The nine categorical variables included household size , sex , alcohol consumption frequency , smoking status , employment status , income , obesity status , overall health  and degree of urbanisation . Further details of the coding of the variables in each dataset are provided in the We adjusted for participation bias in the UKBB using probability weighting36 to predict UKBB participation , conditional on the harmonized auxiliary variables described above. We included 14 main effects  in the model. All categorical and binary variables were entered as dummy variables, indexing each possible level of the variable. In addition, we included all possible two-way interaction terms among the dummy and continuous variables, resulting in 903 included predictors. LASSO performs variable selection by shrinking the coefficients for variables that contribute the least to prediction accuracy. The shrinkage is controlled by the tuning parameter (λ), which was obtained using fivefold cross-validation that minimizes the cross-validated error.To derive the model for participation probability, we first combined the harmonized UKBB data with the data from the reference sample (HSE). We then used LASSO regression in glmnetPi) were then used to build the individual sampling weights (wi). The weights were calculated as an extension of standard inverse probability weights (wi = (1 − Pi)/Pi), designed to make the weighted sample estimates conform to the population estimates35. To assess the performance of the generated weights, we evaluated the extent to which the weighting recovered means (for continuous variables) and prevalences (for binary traits) in the UKBB and hence mitigated participation bias. We also quantified participation bias as the differences between the correlations among all auxiliary variables within the UKBB (rUKBB) and the HSE (rHSE). The degree to which the weighted correlations  reduced bias was estimated as /(|rHSE − rUKBB|), where a value of one indicates that weighting fully eliminated bias. The weighted means (and proportions) for a given variable (Xi) were estimated using the weights (wi), with the expression The predicted probabilities . Here we used the training sample (80% of the data) for model estimation and the test sample (20% of the data) to generate the out-of-sample predicted probabilities. The degree of participation bias reduction was then compared between the out-of-sample predicted probabilities and the full-sample probabilities.nmissing < 50,000). Some of these traits  were used in the model deriving the probability weights. The coding of all variables, genotyping, imputation and quality control procedures are described in the P > 1 × 10−6, minor allele frequency > 1% and call rate > 90%).To evaluate the extent to which SNP effects were distorted by participation bias in the UKBB, we conducted wGWA analyses. wGWA was performed for 19 UKBB health-related traits collected at baseline with few missing observations . The standard least squares estimate of the variance is based on the assumption of homoskedasticity . Since the use of sampling weights violates this assumption, we used the Huber–White estimator39 to estimate the variance of the coefficients:Y denotes the phenotypic outcome vector, W is a diagonal matrix with the probability weights sitting on the diagonal and X is a column vector of the genotype values.We obtained unweighted SNP estimates 41, as well as the Neale Lab42 discussing its application specifically when using UKBB data.Both models included the same covariates . We applied a linear model to all outcomes (continuous and binary traits). This was done to allow for the standardization of SNP estimates and to ensure the comparability of effect sizes. A more detailed discussion on the advantages and disadvantages of using a linear over a logistic model for binary outcomes is provided by von Hippel23, autosomal heritability linked to biological sex could result from sex-differential participation. As such, reduced heritability estimates in wGWA compared with GWA would provide evidence for the utility of wGWA for participation bias correction. In addition, we compared the resulting SNP effects with the effects of previously identified sex-associated variants (P < 5 × 10−8). Here 49 variants assessed in an independent sample of >2,400,000 volunteers curated by 23andMe23 were selected.Two additional sets of analyses were conducted to explore the genetic basis of UKBB participation. First, we conducted autosomal wGWA and standard GWA on biological sex and evaluated whether wGWA reduced sex-differential participation bias. As previously suggestedP < 5 × 10−8) were selected via clumping . PhenoScanner44, a database of genotype–phenotype associations from existing GWA studies, was used to explore previously identified associations of lead SNPs with other phenotypes. Genetic correlations with other traits were estimated using LD-score regression45 as implemented in the R package GenomicSEM46. The summary statistic files used in LD-score regression were obtained for 49 health and behavioural phenotypes, using publicly available summary statistic files accessible via consortia websites or the MRC-IEU OpenGWAS project (https://gwas.mrcieu.ac.uk)47 . For binary phenotypes, the observed scale was converted to the liability scale49, where the population prevalence was set to be equal to the weighted prevalence in the UKBB. We also estimated bivariate genetic correlations among all phenotypes included in standard GWA and wGWA . To compare the estimates obtained from wGWA and standard GWA, we calculated the difference :SNP heritability estimates were obtained for both the standard GWA and wGWA output  were obtained from 200-block jackknife analysis. For this, we split the genome into 200 equal blocks of SNPs and removed one block at a time to perform jackknife estimation.The correlation coefficients 50) SNPs reaching genome-wide significance (P < 5 × 10−8) in either wGWA or standard GWA for a given phenotype. Phenotypes with few (<10) genetic instruments were not included in the MR analyses. We used the inverse-variance weighted (IVW) MR estimator, which combines the ratio estimates of the individual genetic variants Gj to derive the causal effect 51 to account for selected genetic variants m refers to the number of SNPs selected as instruments. The corresponding variance was estimated as To evaluate the impact of selection bias when using MR, we assessed whether sample weighting altered MR estimates. As genetic instruments, we selected LD-independent  an MR estimate using the SNP effects from standard GWA analyses and (2) an MR estimate using the SNP effects from wGWA analyses. We included in MR the standardized SNP effects and standard errors , which were derived using the following formulaαDIFF (Z = αDIFF/s.e.(αDIFF), whereTo compare the standard (Further information on research design is available in the Supplementary InformationSupplementary Sections 1–5 and Figs. 1–9.Reporting SummaryPeer Review FileSupplementary TablesSupplementary Tables 1–11.


High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data.Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type.ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.ChemGAPP is available at  The field of chemical genomics and high-throughput phenomic profiling has revolutionized the ability to functionally annotate unknown genes on a genome-wide level. With applications for drug discovery, mechanism of action studies, and conditional essentiality studies, chemical genomics is a rapidly growing field . Chemicahttps://sourceforge.net/projects/emap-toolbox/); however, this tool is now deprecated , which has been developed as a comprehensive chemical genomics data analysis software.Despite the rise in chemical genomics studies, there are currently no dedicated programs to analyse the type of data these screens produce. Currently, a variety of methods are being implemented for the analysis of data and are being performed by in-house scripts or are adaptations of packages used for similar techniques , 2021. Aprecated . Furtherprecated , 2021. CHere, we provide evidence for ChemGAPP’s effectiveness to reliably analyse chemical genomics data. By providing ChemGAPP it will enable the wider scientific community to use chemical genomics approaches to reveal significant, biologically relevant insights into gene function, drug mechanisms of action, and antibiotic resistance mechanisms.ChemGAPP comprises of three pipelines ChemGAPP Big, ChemGAPP Small, and ChemGAPP GI, each specifically designed for different types of chemical genomic screens .Escherichia coli. Nichols et al. assessed the phenotypes of the KEIO collection, an in-frame single-gene knockout mutant library in E.coli K-12, within over 300 conditions . The distribution of the population is adjusted to a standard normal distribution, with a mean of zero and a std of 1 . The Z-sP-value calculated. The mean P-value for each replicate was calculated by averaging the Mann–Whitney P-values for comparisons including the replicate of interest.The Mann–Whitney test compares the distribution of colony sizes between replicate plates. If two replicate plates are reproducible, they should fall into the same distribution. This test is particularly useful for identification of mislabelled plates since distributions of colonies will significantly differ. Furthermore, the Mann–Whitney test can highlight unequal pinning effects. For each replicate plate in the condition of interest, the distribution of mutant colony sizes was compared to each other replicate’s distribution and a Mann–Whitney P-values for each replicate plate. A large variance here suggests that the entire condition may be detrimental, since it is likely all replicate distributions differ from each other. Finally, a general variance analysis was employed. Here, the variance of each replicate colony in the condition of interest was calculated. The mean of these variances was then calculated to gain an average variance across all plates for each condition. A high variance implies that most colony replicates differ within their size, and thus the entire condition is non-reproducible.Where all replicate plates are non-reproducible, a condition can be deemed unsuitable for use. This can occur, for example, when a condition produces a coloured media which is difficult to distinguish from the colonies. Here, Iris can struggle to differentiate the colonies from the background media, leading to artefacts in detection . TherefoCuration of the dataset is a fine balance between removing detrimental data and remaining informative. Therefore, the thresholds at which data are removed are left to the user. However, ChemGAPP Big allows for an informed choice by outputting a visual representation of the quantity of data lost at different thresholds for the various tests . For theT-test where the mean of a set of colony replicates, for the desired mutant within a specific condition, are compared to the median size of that mutant across all conditions. This provides a statistical score for fitness, with positive scores showing increased fitness and negative scores showing decreased fitness. Comparing a mutant to itself across all conditions as a simulated wildtype is more powerful than comparing to the true wildtype. This is because a mutant may have generally increased or decreased fitness than the wildtype. By comparing to itself, a true baseline for comparison is used. Furthermore, since S-scores correlate to statistical significance, they provide a robust and informative measurement of how strong a phenotype is across various conditions.In order to find meaning within the data, fitness scores must be produced. Within ChemGAPP Big, this is performed using the S-score test. The S-score test, as described in Where S-scores were calculated as infinity, scores were converted to missing values. The S-scores of each plate were then scaled such that the interquartile range (IQR) of the scores was equal to 1.35. This scales the datasets such that the values for significant hits are for S-scores <−3 and >2. In general, the majority of S-scores within a dataset will be close to zero. Therefore, significant hits are S-scores that are classified as outliers. Outliers are commonly determined as scores deviating from the first or third quartiles by 1.5 times the IQR, which in this case is ∼2. Therefore, significant scores would be classified as <−2 and >2. However, since loss of function mutants are more common, a higher threshold of deviation was chosen to represent significant negative S-scores. The final non-curated dataset was visualized within Treeview3 . Data weFollowing dataset curation, it is vital to confirm which dataset has the most accurate fitness scores. In order to measure the accuracy of the datasets, the concept that genes within the same operon often have a similar function was drawn upon. If genes are functionally related, we would expect their knockout mutants to behave similarly across the different tested conditions. Thus, they would have similar phenotypic profiles, which is their set of S-scores across all conditions . TherefoIn order to evaluate the accuracy and robustness of the S-scores produced by ChemGAPP Big, a bootstrapped dataset was produced. The KEIO original and curated normalized datasets were bootstrapped 1000 times with replacement. Each of the 1000 datasets was subjected to S-score analysis. The mean of the bootstrapped S-scores for each mutant in each condition, were compared to the true S-scores and the mean absolute error (MAE) calculated.In large scale chemical genomics, by design, the screens are unbiased, and the aim is to test mutant libraries against a manifold of stresses. Since in most conditions mutants are unlikely to display a phenotype, a normal distribution of colony sizes is achieved. In contrast in small scale studies a few or only one condition are screened, and, in such analyses, a confined hypothesis is often being tested. When testing a specific hypothesis, conditions are often specifically chosen with the assumption that there will be an associated phenotype. Therefore, the conjecture about the normal distribution of fitness effects across conditions may not hold. In these instances, ChemGAPP Big is not fit for purpose.Therefore, we developed ChemGAPP Small for the analysis of targeted small-scale chemical genomics studies. In ChemGAPP Big fitness values are computed based on a minimum of ∼10 conditions with normally distributed colony sizes. In contrast, ChemGAPP Small generates fitness ratios for mutants versus the wildtype on the corresponding condition, in place of S-scores, without relying on any assumption about fitness distributions across conditions. By doing so, ChemGAPP Small allows users to robustly analyse smaller datasets without any constraints on gene or condition number. The small-scale Iris files were compiled and normalized as for ChemGAPP Big; however, zero values were not converted to missing values. Fitness ratios were computed for each plate, either by dividing the mean mutant colony size by the mean wildtype colony size for the bar plots and heatmap, or by dividing each individual colony by the mean wildtype colony size for the swarm plots. Significance for bar plots was measured by 95% confidence interval, whereas a one-way Analysis of Variance (ANOVA) was used to determine significant differences from the wildtype within the swarm plots.Genetic Interactions) can analyse genetic (epistatic) interaction screening data. Genetic interaction studies aim to determine the type of epistasis between two genes. ChemGAPP GI analyses single or multiple gene pairs on the same condition plate, producing separate plots for each gene pair. Genetic interactions are defined as instances where the observed fitness of a double knockout mutant is significantly different to the expected fitness ratio  lipoprotein hineries . Banzhafd ratios . ChemGAPectively . Furtherachieved . ChemGAPChemGAPP was designed to address the need for a chemical genomics analysis software that is easy to use and fully suited to purpose. The current lack of software has led to the implementation of in-house scripts, often based on EMAP Toolbox , 2021. TWe have introduced ChemGAPP a comprehensive, user-friendly wrapper software dedicated to the analysis of a variety of chemical genomic screen types. ChemGAPP’s three sub-packages were specifically designed to allow a wider scientific audience to engage in chemical genomic techniques by streamlining each dedicated analysis pathway.btad171_Supplementary_DataClick here for additional data file.


Cardiovascular diseases (CVDs) are the leading cause of deaths globally. Mortality and incidence of CVDs are significantly higher in people with mood disorders. About 81.1% of CVD patients were reported with comorbidities in 2019, where the second most common comorbidity was due to major depressive disorder (MDD). This study, therefore, aimed to evaluate the genetic correlation between CVDs and mood disorders by using data from the UK Biobank towards understanding the influence of genetic factors on the comorbidity due to CVDs and mood disorders.The UK Biobank database provides genetic and health information from half a million adults, aged 40–69 years, recruited between 2006 and 2010. A total of 117,925 participants and 6,128,294 variants were included for analysis after applying exclusion criteria and quality control steps. This study focused on two CVD phenotypes, two mood disorders and 12 cardiometabolic-related traits to conduct association studies.The results indicated a significant positive genetic correlation between CVDs and overall mood disorders and MDD specifically, showing substantial genetic overlap. Genetic correlation between CVDs and bipolar disorder was not significant. Furthermore, significant genetic correlation between mood disorders and cardiometabolic traits was also reported.The results of this study can be used to understand that CVDs and mood disorders share a great deal of genetic liability in individuals of European ancestry. CVDs are the leading cause of death globally. An estimated 18.6 million people died from CVDs in 2019, representing 32% of all global deaths. About 90% of CVDs were attributed to ischemic heart disease, cerebrovascular disease, peripheral artery disease and atrial fibrillation  rs1006737 in CACNA1C was correlated to a higher risk of bipolar disorder  was 30–60%, that of MDD was 31–42% and that of bipolar disorder was 85–89%  for bipolar disorder, MDD and CAD was associated with higher levels of neuroticism  and myocardial infarction . A multivariable MR analysis also presented robust and consistent results  and myocardial infarction  using 93 genetic instruments, which was confirmed via sensitivity analyses using 34 genetic instruments  analysis estimates the causal effect of the exposure on the outcome under study, using genetic instrument variables . Some prior studies utilized MR and demonstrated that MDD conferred a causal effect on the risk of cardiovascular conditions, while no reverse causation of cardiovascular condition towards risk of incidence of MDD was observed  individual call rate <99%, (2) extremely large or small heterozygosity rate and (3) identity-by-descent status >0.4 were excluded . SNPs wiA total of 117,925 participants were identified and 6,128,294 variants were abstracted for analysis after applying the above exclusion criteria and quality control steps. This study focused on two CVD phenotypes , two mood disorders (bipolar disorder and MDD) and 12 cardiometabolic-related traits , cholesterol, triglycerides, high-density lipoprotein (HDL), low-density lipoprotein (LDL), systolic blood pressure (SBP) and diastolic blood pressure (DBP) to conduct an association study. The detailed definition of each phenotype, disorder and trait is included in Table S1.P < 5 × 10−8. The analysis was conducted in PLINK v2.0 (https://www.cog-genomics.org/plink2).All phenotypes were subjected to a GWAS after quality control. Using an additive model, we encoded each variant according to the number of minor alleles it contained . We used linear regression to calculate the effects of the allele frequency on the continuous variables . We used logistic regression to calculate the effects of the allele frequency on the categorical variables . We used principal components analysis to check for population stratification and conducted regression analysis adjusted by age and principal components 1–20. The threshold for genome-wide significance was et al. in 2015, which uses the test statistic of loci in the GWAS to create an LDSC for conducting regression analysis  regression, developed by Bulik-Sullivan This study used the 1000 Genomes Project with European ancestry as a reference panel for LDSCs. The major histocompatibility complex (MHC) on chromosome 6 and gene loci with MAF < 0.01 were excluded. The linkage disequilibrium (LD) in the MHC region is complex, leading to inconsistent LDSCs in this locus. Loci with MAF < 0.01 were excluded because low MAFs can cause extreme test statistics that induce bias into the evaluation of LDSCs.h2) is defined as the proportion of the variation in a phenotype that can be explained by the GWAS variant and ranges from 0 to 1. If the heritability is equal to 0, the variant does not explain the phenotype; if it is equal to 1, the variant determined the phenotype. The regression coefficient (rg) estimated by LDSC is the genetic covariance. The genetic correlation of two phenotypes  can be obtained by dividing the genetic covariance by the respective heritability of the two phenotypes and then taking the square root. The range of genetic correlation is −1 to 1, where −1 represents a negative relationship and 1 represents a positive relationship. The threshold for statistical significance was P < 0.05. The above analysis was conducted using the Python package of ldsc v1.0.1 (https://github.com/bulik/ldsc).Heritability  and of the quality control steps for individual samples , 117,925 participants with qualifying genetic data remained  and arrhythmia , as well as mood disorder phenotypes including MDD  and bipolar disorder . Two cardiometabolic traits were categorical, including diabetes mellitus  and hypertension . For the 10 continuous cardiometabolic traits, the average values were weight 78.46 kg, BMI 27.38 kg/m2, body fat percentage 31.46%, WHR 0.87 cm, cholesterol 5.73 mg/dL, triglycerides 1.73 mg/dL, HDL 1.47 mg/dL, LDL 3.57 mg/dL, SBP 140.88 mmHg and DBP 82.23 mmHg.After application of the exclusion criteria to the UK Biobank database (remained . After SλGC = 1.0988). This deviation was insignificant in the context of this sample size. LDSC regression indicated that the deviation from null was due to polygenic structure, with h2SNP accounting for ∼2% of the overall variance in CVD (h2SNP = 0.0185 [SE 0.0036]) rather than inflation due to unconstrained population structure (LD regression intercept = 1.0472 [SE 0.0065]). We observed one independent genomic locus exhibiting genome-wide significant associations with CVD on chromosome 9  and MDD specifically , MDD  or bipolar disorder , hypertension , BMI  and WHR . We found moderate genetic correlations between CVDs and weight , body fat percentage , triglycerides , SBP  and DBP . However, we found a negative genetic correlation between CVDs and HDL , along with small, negative but non-significant correlations between CVDs and total cholesterol  and LDL . Furthermore, mood disorders were also identified as having genetic correlation with seven cardiometabolic traits: diabetes mellitus  and hypertension  had moderate correlations; BMI , body fat percentage , WHR  and triglycerides  had small correlations. In addition, HDL  had a significant negative correlation. Additional results of the genetic correlations of the 12 cardiometabolic traits with arrhythmia and MDD are presented in Table S3.Finally, we conducted analyses of the genetic correlation of the 12 cardiometabolic traits  with CVDs and mood disorders. The results are presented in P = 1.00 × 10−10) and three independent loci associated with mood disorders with suggestive significance . We also identified an SNP-based heritability estimate for CVDs of ∼2% and mood disorders of ∼2%. There was a strong genetic correlation between CVDs and mood disorders, indicating substantial genetic overlap between CVDs and susceptibility to mood disorders. Furthermore, there was also a significant genetic correlation between mood disorders and cardiometabolic traits such as diabetes mellitus, hypertension, BMI, body fat percentage, WHR, triglycerides and HDL. Thus, we observed a genetic overlap between mood disorders and both CVDs and cardiometabolic traits.In this study, we have identified one independent locus associated with CVD  , Generation Scotland: The Scottish Family Health Study (GS:SFHS) and the UK Biobank. The results found a positive genetic correlation between MDD and BMI, CAD and type 2 diabetes , CAD, stroke and depression separately but not in an integrated way (Bahrami et al., This study used the UK Biobank as the sole data source, and thus only genetic correlations in individuals of European ancestry could be explored. In a past study, individuals of East Asian ancestry have been found to have negative genetic correlations with CAD, BMI and type 2 diabetes (Giannakopoulou This study found positive genetic correlations between CVDs and mood disorders in individuals of European ancestry. This means that CVDs and mood disorders share a great deal of genetic liability. Genetic correlations between mood disorders and cardiometabolic traits were also found. However, further studies will be needed to explore the causal relationship between CVDs and mood disorders.