Compositional analyses included measurement of proximates, minerals, amino acids, fatty acids, vitamins, and antinutrients. Significant differences between Bt rice and non-Bt rice were detected; however, all differences were within the reference range. The statistical analyses, including analysis of % variability, analysis of similarities (ANOISM), similarity percentage (SIMPER) analysis, and permutational multivariate analysis of variance (PERMANOVA) were performed to study factors contributing to compositional variability. The multivariate analyses revealed that environmental factors more influenced rice components’ variability than by genetic factors. This approach was shown to be a powerful method to provide meaningful evaluations between Bt rice and its comparators. In this study, Bt rice was proved to be compositionally equivalent to conventional rice varieties through multiple statistical methods.The safety of transgenic Bt rice containing bacteria-derived  Analytes representing key nutrients, antinutrients, toxicants, and secondary metabolites from GM crops are compared to data collected from non-GM conventional counterparts. The comparative safety assessment approach based on the concept of substantial equivalence assures that foods and feeds derived from GM crops are as safe as their conventional counterparts.3 Although the application of the concept is not a safety assessment in itself, it leads to identifying potential differences between the GM crops and the conventional crops and to further investigate the safety assessment with respect to their toxicological impact. Besides, it needs to recognize some of the limitations of the concept of substantial equivalence in GM food risk assessment. Kuiper et al.4 have reported practical difficulties may encounter for the application of the principle in particular respect to the availability of the direct comparator, limited information on the appropriate safe conventional comparators in levels of relevant crop constituents, and limited availability of methods for the detection and characterization of unintended effects. Natural variation of nutrients and antinutrients in plant cultivars may vary randomly and cause no harm to human health. However, continuously lower or higher concentration may result in safety concerns despite its natural variation.4 To increase the chances of detecting unintended effects, non-targeted profiling techniques such as transcriptomics, proteomics, and metabolomics have been encouraged to be included.4–8The biosafety of genetically modified (GM) crops is generally determined via a safety evaluation assessment before commercialization. Assessment regulations differ by country but are all implemented based on risk assessment according to their substantial equivalence to non-GM commercial crops.3 It is recommended to cultivate conventional non-transgenic commercial varieties (termed reference) concurrently at the same sites as the GM crop and its direct counterpart in order to obtain an overview of the impact of genetic variation on compositional variability.9 The reference data of conventional crops is also available from the OECD, the ILSI Research Foundation Crop Composition Database (ILSI-CCDB), or from published literature. The evaluation of natural variability in the composition of conventional crops and GM crops has been incorporated into their compositional assessment in several studies, using statistical approaches.10–15 Recently, the empirical distribution curves of compositional end-points for maize and soybean were determined, providing novel information on end-point specific variability relevant to risk assessment.16A significant difference in the amounts of nutritional analytes in GM crops compared to those of their conventional comparators needs to be evaluated within the natural variation of conventional crop components that already possess a history of safe use as foods.Bacillus thuringiensis (Bt) have been developed to control harmful insects, improve crop productivity, and benefit the environment.17–23 In South Korea, several Bt transgenic rice lines that are expressing Cry1 proteins have been developed.24–26 Bt 9 rice contains mCry1Ac1, modified from the original Bt Cry1Ac1 by deleting the C-terminus (Genebank No. AY126450) for insect resistance and bar (which confers herbicide tolerance) as a selection marker.26 It was suggested Bt rice products are compositionally equivalent to their non-transgenic counterparts.15 Recently, Liu et al. showed that unintended changes of Bt rice do not go beyond conventional cross-breeding using the approaches of transcriptomics and metabolomics.16Rice is one of the major staple crops and primary food sources of more than half of the world’s population. Multiple transgenic rice plants with the insecticidal Cry proteins from the bacterium 27 revealed that the analyte levels in Bt rice are similar to those in conventional varieties. We performed multivariate analyses to obtain meaningful data on natural variation among genotypes and variation due to environmental effects and their interactions with genotypes. Multivariate analyses included % variability analysis , ANOSIM, PERMANOVA, and SIMPER. We suggested that a multi-statistical analysis approach would be useful to develop more reliable and effective methods in GMO safety evaluation.In the present study, we employed the substantial equivalence assessment to determine the compositional safety of Bt 9 rice. The analyte levels in the insect-resistant Bt 9 rice were compared with those in its near-isogenic non-Bt rice (direct counterpart). When one or more compositional components in Bt rice significantly differed from those in non-Bt rice, the levels of analytes of concern in Bt rice were compared with those in four non-transgenic commercial rice cultivars (from now on referred to as reference) grown simultaneously with Bt rice and non-Bt rice in two sites for 2 years. In addition, the range of values obtained for all reference rice across sites and years , and the OECD literature dataOryza sativa ssp. japonica ‘Ilmi’), non-Bt rice (Ilmi), and four non-transgenic commercial rice varieties  were simultaneously cultivated in 2015 and 2016. Field sites were in Jeonju  and Suwon  where localized in the central and northern regions of South Korea, respectively. Four commercially available rice varieties with similar genetic backgrounds to ‘Japonica’ were used as reference materials for the GM rice safety assessment. They are high-quality rice varieties developed for their desirable characteristics such as rice taste, disease resistance, and grinding characteristics, and then distributed to farmers by the Rural Development Administration of South Korea. All test samples were planted in May in a strip-plot design with five biological replicates per site, and standard commercial agronomic practices were applied at each field site following local practices. Weather conditions, including rainfall, at the cultivation sites, are presented in Table S1 of the Supporting Information. Grain samples from twelve rice plants were pooled to form one biological replicate and then shipped to the laboratory in ambient condition. Grain samples were dried to a moisture concentration of 9–11% in the sun using the plastic sheet for three days. Samples were manually dehulled using a hulling machine  to produce brown rice grains and subsequently finely ground using a Planetary Mono Mill Pulverisette 6 . Powdered samples of each rice type were immediately stored at −80°C until subjected to compositional analysis.Insect-resistant Bt 9 rice , and insoluble dietary fiber (IDF)), carbohydrates, amino acids, minerals, fatty acids, vitamins, and antinutrients. Three experimental replicates were used per biological replicate, resulting in 15 samples for each analyte.28 The content of nutritional components was calculated on a dry weight basis considering the moisture content. Crude protein content was calculated based on total nitrogen content, using the Kjeldahl method.29 Crude fat was analyzed using the Soxhlet extraction method.30 Crude fiber was analyzed in an automatic furnace using a filter bag.31 For analysis of IDF and SDF analysis, heat stable amylase, protease, and amyloglucosidase were used to dissolve proteins and starches, leaving a fibrous residue.32 IDF was determined by weighing a fibrous residue after filtration. For SDF analysis, the samples filtered was precipitated with ethanol and filtered. After dying, the residue weight was determined. Carbohydrate levels were calculated as 100% − (% moisture + % protein + % lipid + % ash).Moisture content was determined by gravimetric measurements using a hot-air oven set at 105 °C.33 The sulfur-containing amino acids cysteine and methionine were oxidized with performic acid before hydrolysis with 6 N hydrochloric acid.33 The content of individual amino acids was expressed as a percentage of the total protein. The concentrations of copper, iron, zinc, manganese, calcium, sulfate, magnesium, potassium, phosphorus, and sodium were determined using inductively coupled plasma (ICP) optical emission spectrometry .35 Samples were hydrolyzed in a solution containing sulfuric acid and nitric acid before instrumental analysis of ICP. Fatty acid profiles were determined according to the AOCS method Ce 1–62 using a Shimadzu GC-2010 gas chromatograph  and expressed as a percentage of total fatty acid.36 Fatty acids were extracted with a chloroform: methanol (v/v 2:1) solution containing an internal standard (Pentadecanoic acid solution) and then saponified with toluene, 5 N sodium hydroxide, and methanol. The saponification mixture was methylated with 14% boron trifluoride. The resulting methyl esters were resolved in hexane and analyzed.36Sixteen amino acids were directly analyzed using an automatic amino acid analyzer , following protein hydrolysis with hydrochloric acid.1 was measured using a modified method of Sims and Shoemaker, using high-performance liquid chromatography (HPLC) analysis.37 The samples in 0.1 N HCl were incubated at 80°C for 30 min, and the resulting solution was incubated in a buffered enzyme solution (taka-diastase). The solution was purified on a C18 cartridge, and then the filtrate was reacted with potassium ferricyanide to convert thiamine to thiochrome. For vitamin B2 analysis, the rice samples were hydrolyzed with 0.1 N HCl, followed by incubation at 80°C for 30 min. The filtered solution using a 0.45 µm syringe filter was analyzed by HPLC fluorometric detection according to the method described by Esteve et al.38 For Vitamin B3 analysis, the rice samples were autoclaved at 121°C for 15 min in 2.5 N H2SO4 and followed by solid-phase extraction using a cartridge (Oasis HLB Plus LP Extraction Cartridge). N-methyl-N-(trimethylsilyl) trifluoroacetamide and pyridine were added to the resulting solution and then incubated with shacking at 60°C for 30 min for derivatization. Each derivatized sample was injected into the Gas-Chromatography/Time-Of-Flight Mass spectrometry .39 Vitamin B7 (Biotin) was determined according to the MFDS Food Code.40 The samples suspended in 0.01 M KH2PO4 were incubated in a sonication bath for 30 min. The suspension was then centrifuged for 30 min at 9,000 × g, and the supernatant was filtered using a 0.45 µm syringe filter before HPLC analysis. The measurement of vitamin E (α-tocopherol) amount was performed as described by Park et al.21 Samples were suspended in ethanol containing 0.1% ascorbic acid and incubated at 85°C for 5 min, and then after potassium hydroxide (80%) was added to the suspension, samples further incubated at 85°C for 10 min. 5α-cholestane , distilled water, and hexane were added to the chilled suspension. After centrifugation, the upper layer was dried under nitrogen gas. Sample derivatization was performed as described in Vitamin B3 analysis, and then vitamin E was analyzed by GC/TOF.21Vitamin B21 The samples were incubated with 2.4% HCl in a sonication bath for 2 hr, and the supernatant was filtered through polyprep-prefilled chromatographic columns  containing an AG-1-X8 anion exchange resin . Trypsin inhibitor activity was determined using the AOCS method Ba 12–75.41 The rice sample was extracted in 0.01 N sodium hydroxide for 3 hr in a sonication bath, and then varying aliquots of the sample suspension were mixed with a known amount of trypsin and the synthetic substrate, benzoyl-DL-arginine-p-nitroanilide (BAPNA). After the mixture was incubated for 10 min at 37°C, the reaction was stopped by the addition of acetic acid. The solution was filtered with a 0.45 µm syringe filter, and then the absorbance was measured at 410 nm.The content of phytic acid was determined as described by Park et al.Statistical analysis of compositional data. Statistical analyses of the data were carried out using the SAS 9.2 software package . A statistical difference test for Bt rice and non-Bt rice was performed on individual data sets obtained from each site per year, using the independent two samples Student’s t-test. In order to identify the effects of genotype and environment (location and cultivation year) on the natural variation in nutritional components, we evaluated the random effect of varieties (G), site (S), cultivation year (Y), and their interactions (G × S × Y) in nutritional variation, using the linear mixed model with random intercepts fitting by restricted maximum likelihood (lme4 package) in R studio (version 4.0.2). It included three variables  for the variety, two variables  for the site, and two variables  for the year. Variance components were estimated with the structure:Analyte ≈ G + S + Y + GSY + ƐWhere analyte is the composition level, G is the genotype, S is the site, Y is the cultivation year, GSY is the genotype × site × year interaction, and Ɛ is the general error term.The syntax is as follow:fm1<-lmer(y ~ 1+(1|Site:Variety:Year)+(1|Site)+(1|Variety)+(1|Year), data).42 Analyses of ANOSIM, PERMANOVA, and SIMPER were performed to define the explanatory power of the variability among the data.43–45 Two-way ANOSIM was used to calculate the distance between study groups according to the Bray-Curtis similarity matrix. The Global R-value indicates the similarity among groups, showing a range of −1 to +1. Global R-values close to zero indicate little or no difference among groups, while R-values closer to −1 or +1 indicates significant differences among groups. PERMANOVA utilizes the permutation method to test inter-group differences and examines whether between-group variance explains a significant proportion of the total variance in the system. In this study, PERMANOVA based on 999 permutations using the Euclidean distance and partitioning was done using Type I sum of squares for each factor . The significance level of the sample statistic used in PERMNOVA was 0.1%. The SIMPER test was used to determine the average dissimilarity between pairs in the study groups and the contribution (%) of each analyte to the dissimilarity between pairs. It was calculated using the Bray-Curtis matrix, and the cutoff value was 80%.The variance component parameters were divided by the total variance to get the components’ variance proportions. The Plymouth Routines in Multivariate Ecological Research (PRIMER) software package version 6.0 with Add on PERMANOVA  was used for multivariate analysis of variance to identify significant differences among study groups regarding their nutritional profiles, following the method described by Anderson.Compositional analysis results (55 components) for Bt rice and non-Bt rice are presented in Table S2 of the Supporting Information as of the mean value with ± standard deviation (SD), together with their ranges . The reference ranges  of each analyte for commercial rice were determined from 20 means of four rice varieties  grown simultaneously with Bt rice and non-Bt rice. The all reference ranges  of each analyte were obtained from the reference ranges across two cultivation sites for two years. Boxplots of component values in Bt rice, non-Bt rice, reference, and all reference are presented in Figure S1-S5.p < 0.05). From a total of 220 comparisons, 53 (24.1%) were significant; these included 20 comparisons from Suwon and 33 from Jeonju . However, most of the analytes that exhibited significant differences between Bt rice and non-Bt rice were within the ranges of commercial rice varieties planted simultaneously (reference) or were found to be within the ranges reported in the OECD, with the exception of moisture, sulfate, stearate, carbohydrate, and IDF content. Bt rice moisture content was significantly lower (p <0 .05) compared with non-Bt rice from both Suwon and Jeonju in 2016. Bt rice moisture content was outside of the reference ranges but were all within the ranges of all reference and of reported in OECD literature. Sulfate and stearate were also significantly lower in Bt rice than in non-Bt rice cultivated in 2015 at Suwon (p < 0.05). Sulfate was not within the reference range, but it was within the reported OECD range. Stearate was not within the reference range, but it was within the all reference range and the reported OECD range. For carbohydrates, a statistical difference (p < 0.05) was observed between Bt rice and non-Bt rice in 2016 at Jeonju. Bt rice from 2016 Jeonju was not within the ranges of references as well as the OECD range. However, it was within the all reference range. The level of carbohydrate was calculated from 100% − (% moisture + % protein + % lipid + % ash). Since mean values of moisture, protein, lipid, and ash in Bt rice were equivalent to conventional crops, it is reasonable to consider biologically no meaningful difference in carbohydrate. Statistical difference (p < 0.05) was observed in the mean IDF content between Bt rice and non-Bt rice in 2016 at Jeonju. Information about the IDF content in brown rice was not available in the OECD literature.27 However, the mean IDF content in Bt rice from Jeonju in 2016 was within the all reference range, suggesting that IDF in Bt rice is biologically equivalent to that in conventional crops (Table S2 of the Supporting Information). Taken together, given the paucity of observed differences, the grain of Bt rice was compositionally equivalent to that of conventional comparators.Overall, a few statistically significant differences between Bt rice and non-Bt rice was characterized  and yield in US soybean seeds through meta-analysis and synthesis of a database obtained across the United States of America.49 Oh et al. reported that the growing site and cultivation year contributed more to compositional variability in 15 Korean commercial rice varieties through statistical studies.5010We performed a percentage variability analysis to determine to what extent components were affected by genotype, site, and cultivation year, as well as their interaction (G × S × Y). This model was previously reported as suitable for field studies by describing the impact of random effects on nutritional variation and determining overall differences and equivalences among samples.The results of percentage variability are presented in Potassium (18.08%) and zinc (14.98%) levels displayed the highest variability, contributed by genotype and site, respectively. The cultivation year and the G × S × Y effect mainly contributed to the total variance for calcium, phosphorus, iron, manganese, and sodium. Residuals determined relatively high proportions of the total variance in fatty acids and contributed to more than 30% of the total variance in all lipids, with the exception of myristate and palmitate. Myristate and palmitate contents were highly determined by genotype, which accounted for 67.2% and 43.7% of the total variance, respectively. Variance in oleate (33.3%) and arachidate (36.5%) was also significantly contributed by genotype.With respect to the percentage variability in vitamins, niacin (54.7%) and biotin (42.1%) were mainly affected by cultivation year, while riboflavin (48.1%) was influenced by the G × S × Y effect. Cultivation year (33.6%) and genotype (29.8%) contributed to the variation in thiamin in similar proportions, followed by the G × S × Y effect (18.3%). Genotype and cultivation site highly determined the variability of γ-tocopherol and δ-tocopherol, which accounted for 59.1% and 49.2% of the total variance, respectively. However, residuals notably contributed to α-tocopherol and β-tocopherol, accounting for approximately 60% of the total variance. Phytic acid content was highly determined by the G × S × Y effect (70.7%), while cultivation year (39.4%) and residuals (37.5%) contributed to the variance in trypsin inhibitor content. Collectively, the natural variation in rice composition analyzed in the present study was contributed by environmental factors and the G × S × Y effect as well as genotype.36Significant differences in cultivation year, site, and genotype among the study groups were further tested using the PRIMER statistical package. ANOSIM and SIMPER are multivariate analyses that use resemblance matrices, with SIMPER decomposing the average Bray-Curtis dissimilarities between all sample pairs.p-value). Large R-values (close to +1 or −1) indicate greater dissimilarity between the two groups. Overall, in the ANOSIM test, genotype factors showed R values lower than those of year vs. year for most nutritional components, indicating a lower contribution of genetic factors than the year effect. Cultivation year exhibited the greatest influence on the composition of proximates  and amino acids , followed by non-Bt rice vs. Ref, and lastly by Bt rice vs. Ref. In addition, vitamins and minerals were different between the plant-growing years . However, cultivation sites contributed to nutritional compositions to a lesser extent compared with cultivation years. There were significant differences in proximates , fatty acids , and minerals  of Bt rice vs. non-Bt rice. R-values of Bt rice vs. non-Bt rice were lower for proximates and minerals than those of Bt rice vs. Ref, indicating that difference between Bt rice and non-Bt rice is smaller than that of between Bt rice and Ref. For example, non-Bt rice vs. Ref exhibited the greatest impact on the composition of proximates , followed by Bt-rice vs. Ref  and lastly by Bt rice vs. non-Bt rice . There was no significant difference in antinutrients by the genotype factors. Bt rice vs. non-Bt rice did not exhibit significant differences (p = 0.05) in amino acids, vitamins, and antinutrients. R-value of fatty acids  of Bt rice vs. non-Bt rice was slightly lower than that of Bt rice vs. Ref .The ANOSIM results are shown in p (perm) values and COV are shown in p < 0.01) indicates a greater degree of influence of each factor.PERMANOVA and pairwise comparisons were used to determine whether nutritional components were significantly affected by existing environmental and genotypic factors, as well as by interactions among these factors. The variation (COV) component in PERMANOVA was a value that represented the effect of each factor on nutritional variance; the larger the COV value, the greater the effect of a particular factor on the differences observed among the study groups. In contrast to ANOVA, which relies on normal distributions, PERMANOVA is a nonparametric analysis of variance using distances between samples and uses a permutation method to test the hypothesis. PERMANOVA results of 50 Cultivation year (COV = 3.299) played the largest role for vitamin. The G × S × Y effect (COV = 1.092) also influenced vitamin. Cultivation year (COV = 0.196) was the most significant factor accounting for the levels of antinutrients , followed by the G × S × Y effect (COV = 1.986) and then genotype (COV = 1.236). For amino acids, the G × S × Y effect (COV = 0.317) had the most influence, followed by cultivation year (COV = 0.195) and then genotype (COV = 0.185). Cultivation year (COV = 0.205) and genotype (COV = 0.128) had significant roles for fatty acids. With regards to the mineral contents, there was a significant association with cultivation site (COV = 0.651) and the G × S × Y effect (COV = 0.607). Cultivation year (COV = 0.113) and genotype (COV = 0.154) were also had significant influences on mineral. In agreement with our data, the variability of rice mineral compositions was attributable to the plant cultivation site rather than to the variety or cultivation year.utrients .The average dissimilarity between pairs of study groups and the relative contribution of each analyte to the observed dissimilarity was determined by the SIMPER test. The average dissimilarity in study groups for the nutrient category is shown in The SIMPER results for amino acids revealed that tryptophan, valine, and tyrosine mainly determined dissimilarities among all study groups . The disp < 0.05) in 19 components from Suwon and 32 components from Jeonju for either of the two years or both (Table S2 of the Supporting Information). However, the mean values of these components in Bt rice were within the reference or OECD ranges, indicating the compositional equivalence of Bt rice and conventional comparators. The impact of both genetic and environmental factors on nutritional variability and degree of similarity/dissimilarity among the study groups in rice grains was demonstrated by multivariate analysis. The content of rice grain components was more influenced by the cultivation year than by other factors. Results obtained from ANOSIM and SIMPER analyses were complementary, explaining the factors responsible for the differences among study groups, and thus provided a high degree of reliability. In addition, SIMPER analysis identified which nutrient components primarily contributed to the differences observed. We showed that the application of ANOSIM, PERMANOVA, and SIMPER is of value in understanding the difference and similarity of Bt rice against conventional rice in the context of natural variation. These approaches can be effectively applied in future safety assessments of crops developed by new breeding technologies, as well as GM crops.In the present study, Bt rice exhibited statistically significant differences compared with non-Bt rice (Click here for additional data file.


Existing tabular formats ambiguously or incompletely store information about genetic variants and associations, lack essential metadata and are typically not indexed yielding poor query performance and increasing the possibility of errors in data interpretation and post-GWAS analyses. To address these issues, we adapted the variant call format to store GWAS summary statistics (GWAS-VCF) and developed open-source tools to use this format in downstream analyses. We provide open access to over 10,000 complete GWAS summary datasets converted to this format (The online version contains supplementary material available at 10.1186/s13059-020-02248-0. P value) has enabled a range of important secondary research applications including causal gene and functional variant prioritisation [The GWAS is a powerful tool for identifying genetic loci associated with any trait, including diseases and clinical biomarkers, as well as non-clinical and molecular phenotypes such as height and gene expression  (eQTLs).tisation , causal tisation , pathwaytisation , causal tisation , risk prtisation , genetictisation  and heritisation . HoweverP value), imprecision is introduced reducing subsequent test power. Varying field names are easily addressed in principle, but the process can be cumbersome and error-prone. Third, data are frequently distributed with no or insufficient metadata describing the study, traits and variants  which can lead to errors, impede the integration of results from different studies and hamper reproducibility. Fourth, querying unindexed text files is slow and memory inefficient, making some potential applications computationally infeasible .Historic lack of a common standard has resulted in GWAS analysis tools outputting summary statistics in different tabular formats  developed a tab-separated values (TSV) text format with a minimal set of required  columns along with standardised headings [Some proposals for a standard tabular format have been made. The NHGRI-EBI GWAS Catalog  and a file body containing variant-level  and sample-level (one sample per column) information. We adapted this format to include GWAS-specific metadata and utilise the sample column to store variant-trait association data  the specification version number, (2) information about the reference genome assembly and contigs and (3) information  about the fields used to describe variants and samples (or variant-trait associations in the case of GWAS-VCF) in the file body. We take advantage of the VCF file header to store additional information about the GWAS including (1) source and date of summary statistics, (2) study IDs  and (3) description of the traits studied  as well as the source and version of trait IDs .Unlike VCF where a row can contain information about multiple alternative alleles observed at the same site/locus (and thus may store more than one variant), the GWAS-VCF specification requires that each variant is stored in a separate row of the file body. Each row contains eight mandatory fields: chromosome name (CHROM), base-pair position (POS), unique variant identifier (ID), reference/non-effect allele (REF), alternative/effect allele (ALT), quality (QUAL), filter (FILTER) and variant information (INFO). The ID, QUAL and FILTER fields can contain a null value represented by a dot. Importantly, the ID value (unless null) should not be present in more than one row. The FILTER field may be used to flag poor-quality variants for exclusion in downstream analyses. The INFO column is a flexible data store for additional variant-level key-value pairs (fields) and may be used to store for example population frequency (AF), genomic annotations and variant functional effects. We also use the INFO field to store the dbSNP  locus idP value was faster using TSV . However, when the number of variants stored in the GWAS-VCF was 0.5 million, uncompressed text was faster for single position and rsid lookups but not interval queries  and encourage other centres to deploy their own instance . Further, the gwasglue R package provides convenient programming functions to automate the preparation of genetic association data for a range of downstream analyses  to highlight records below prespecified thresholds if the exact value is unimportant. For example, all variants below genome-wide significance (P < 5e−8) or a more relaxed threshold (e.g. P < 5e−5).Our simulation studies demonstrated the GWAS-VCF was substantially quicker when the GWAS was densely imputed (8–44×) than TSV using standard UNIX tools for extracting records by genomic position. Although the GWAS-VCF was slower for extracting records by association A limitation of the current summary statistics formats, including GWAS-VCF, is the lack of a widely adopted and stable representation of sequence variants that can be used as a universal unique identifier for the said variants. Published summary statistics often use rsids  to identAnother potential limitation is the use of multiple ontologies to describe the GWAS trait which might make inter-study comparisons difficult. However, we feel enforcing a specific trait identifier system could prevent the new ontologies and non-human data which would provide a barrier to adoption.Here, we present an adaptation of the VCF specification for GWAS summary statistics storage that is amenable to high-throughput analyses and robust data sharing and integration. We implement open-source tools to convert existing summary statistics formats to GWAS-VCF, and libraries for reading or querying this format and integrating with existing analysis tools. Finally, we provide complete GWAS summary statistics for over 10,000 traits in GWAS-VCF. These resources enable convenient and efficient secondary analyses of GWAS summary statistics and support future tool development.The specification was developed through the experience of collecting and harmonising GWAS summary data across two research centres at scale  and perfP value  or 1-Mb genomic interval. Tests were undertaken with 100 repetitions using BGZIP [Densely imputed summary statistics  for GWAS of body mass index using data from the UK Biobank were obtained from Neale et al. . The datng BGZIP  GWAS-VCFAdditional file 1: Figure S1. VCF format adapted to store GWAS summary statistics (GWAS-VCF). Example GWAS-VCF with individual sections labelled. Table S1. Data fields in the GWAS-VCF. Required and optional GWAS-VCF fields with descriptions as defined in the file specification. Table S2. Open-source tools for working with GWAS-VCF. Description of open-source software for working with GWAS-VCF and download links. Table S3. Possible variant identifier schemes for the ID column of GWAS-VCF. Example unique variant identifier schemes and their advantages/disadvantages.Additional file 2. Review history.


Genetic associations link hematopoietic traits and disease end-points, but most causal variants and genes underlying these relationships are unknown. Here, we used genetic colocalization to nominate loci and genes related to shared genetic signal for hematopoietic, cardiovascular, autoimmune, neuropsychiatric, and cancer phenotypes.Our aim was to identify colocalization sites for human traits among established genome-wide significant loci. Using genome-wide association study (GWAS) summary statistics, we determined loci where multiple traits colocalized at a false discovery rate < 5%. We then identified quantitative trait loci among colocalization sites to highlight related genes. In addition, we used Mendelian randomization analysis to further investigate certain trait relationships genome-wide.PTPN22 with Crohn’s disease, NIPA with coronary artery disease and platelet trait variation, and the hemochromatosis gene HFE with altered lipid levels. Finally, we anticipate potential off-target effects on blood traits related novel therapeutic targets, including TRAIL.Our findings recapitulated developmental hematopoietic lineage relationships, identified loci that linked traits with causal genetic relationships, and revealed novel trait associations. Out of 2706 loci with genome-wide significant signal for at least 1 blood trait, we identified 1779 unique sites (66%) with shared genetic signal for 2+ hematologic traits. We could assign some sites to specific developmental cell types during hematopoiesis based on affected traits, including those likely to impact hematopoietic progenitor cells and/or megakaryocyte-erythroid progenitor cells. Through an expanded analysis of 70 human traits, we defined 2+ colocalizing traits at 2123 loci from an analysis of 9852 sites (22%) containing genome-wide significant signal for at least 1 GWAS trait. In addition to variants and genes underlying shared genetic signal between blood traits and disease phenotypes that had been previously related through Mendelian randomization studies, we defined loci and related genes underlying shared signal between eosinophil percentage and eczema. We also identified colocalizing signals in a number of clinically relevant coding mutations, including sites linking Our findings provide a road map for gene validation experiments and novel therapeutics related to hematopoietic development, and offer a rationale for pleiotropic interactions between hematopoietic loci and disease end-points. Identifying causal loci and genes from human genetic data is integral to elucidating novel disease insights and therapeutic approaches. Quantitative hematopoietic traits are well studied, although relatively few causal variants and genes have been elucidated , 2. MendGenetic colocalization analysis permits identification of shared regulatory loci, with advances extending the scope of potential studies from two to over 10 traits undergoing simultaneous analysis –5. RecenDevelopmental cell types during hematopoiesis, the process that gives rise to all blood lineages, are relatively well mapped. We hypothesized that shared genetic signal impacting traits from multiple blood lineages might nominate genomic loci related to the stem and progenitor cells that spawned those types of blood cells. This approach is orthogonal to prior data that analyzed patterns in accessible chromatin to define genomic locations affecting multiple blood lineages . For exaBlood traits are related to a number of human disease phenotypes . Blood cHere, we used genetic colocalization to define sites wherein two or more human traits shared genetic signal at genome-wide significant loci. We initially examined blood traits, and later expanded our analysis to include a total of 70 blood, autoimmune, cardiovascular, cancer, and neuropsychiatric traits. We then looked for quantitative trait loci impacting gene expression (eQTL) or exon splicing variation (sQTL) at or near sites of genetic colocalization. Our results identify sites that affect specific cell types during hematopoietic development, and reveal genetic variants underlying trait relationships between blood parameters and disease end-points.6 sites . Analyzed SNPs were identified as genome-wide significant in the largest hematopoietic trait GWAS to date [GWAS summary statistics were obtained from publicly available repositories . There were 29,239,447 genomic sites analyzed for colocalization among the hematologic traits. A total of 1,667,428 harmonized sites were analyzed from GWAS summary statistics for the 70 traits. The decreased number of sites included in this latter analysis resulted in decreased power to detect associations. This was reflected in the maximum number of traits colocalized, which in Fig. g < 0.8) were similar to ‘full’ analyses, including 29,261,510 genomic sites for 17 blood traits  > 0.7, based on empiric simulations results from the creators of this algorithm showing that this conservatively gave a false discovery rate < 5% [http://grch37.ensembl.org/Homo_sapiens/Tools/VEP) to identify coding variants and related gene consequences.We used the Ensembl Variant Effect Predictor .We wanted to assess comprehensively the potential gene expression or splicing changes related to colocalization sites. Thus, we analyzed each colocalization site together with all sites in high linkage disequilibrium (EUR rhttps://bedtools.readthedocs.io) to identify the nearest gene to each SNP. Genes and positions were defined by BioMart (https://grch37.ensembl.org/biomart/martview).We used closestBed , as welhttp://geneontology.org). Statistical significance of GO Biological Process enrichments were assessed using binomial tests and Bonferroni correction for multiple testing. Presented data were those pathways with p < 0.05.We submitted QTLs associated with specific traits for biological pathway assessment using the Gene Ontology (GO) resource  . Present2 < 0.01) in 10,000 kb regions.We created genetic instrumental variables from GWAS summary statistics for blood traits , eczema http://cnsgenomics.com/shiny/mRnd, [et. al. [We used mRnd  to esti[et. al. . None of[et. al. .Data were created and presented using R, Adobe Illustrator CS6 and GraphPad Prism 8.Statistical analyses were conducted using R and GraphPad Prism 8.Our first aim was to validate whether colocalization could effectively capture known trait relationships and genetic correlations between hematopoietic lineages . We perfTo investigate trait relationships, we constructed a heat map to depict the percentage colocalization between trait pairs Fig. b. Hierar2 = 0.91 by quadratic regression with least squares fit). Directly correlated , and inversely correlated trait pairs , essentially always colocalized.We then asked whether our colocalization findings mirrored genetic correlation between hematopoietic traits . Indeed,Several trait pairs fell outside the 95% prediction interval. The majority of these trait pairs included two traits from the same hematopoietic lineage  Fig. c. LineagIn sum, these results validated the notion that colocalization analysis results would mirror genetic correlation, and reflect known relationships among hematopoietic lineages and traits. Interestingly, trait pairs without genetic correlation frequently had some degree of colocalization  > 0.8. This experiment, using 17 quantitative hematopoietic traits, identified 946 colocalization sites for 2 or more traits with a PPFC > 0.7, representing 35% of interrogated loci  related to specific hematopoietic lineages and cell types. For example, loci where white blood cell (WBC), red blood cell (RBC), and platelet counts colocalize might indicate developmental perturbation in hematopoietic stem and progenitor cells (HSCs). We therefore looked for sites of colocalization between these quantitative blood traits, and identified overlapping genome-wide significant QTLs. Indeed, QTLs related to these loci pointed to known HSC regulatory genes SH2B3 , 31, ATM2B3 [ATM , and HBSBS1L-MYB   but file RHD , HBZ [35HBZ [LPL , which cLPL [SP1 , ESR2 [3 [, ESR2 , and FAN2 [FANCA  of theseA [STIM1  and C4BP1 [C4BPA , which iTL [TPM4 , which iTL [TPM4 .To our surprise, pathways analyses of red cell and platelet lineage-restricted colocalization QTLs were not enriched for processes ascribed to hematopoiesis, erythropoiesis, or megakaryopoiesis. This suggests that genes and processes linked to terminal red cell and platelet traits are largely impacted by cellular function and reactivity, rather than developmental perturbations. With notable exceptions whereby causal loci do impact hematopoietic development e.g., , 46–48),–48,46–482 = 0.83, y-intercept = 0.037 ± 0.117).We then applied an extended colocalization analysis to summary statistics for 70 total hematopoietic, cardiovascular, autoimmune, cancer, and neuropsychiatric traits  < 0.8 identified 1670 colocalization sites, with 2.6 ± 1.3 (mean ± SD) colocalizing traits per locus  were asETS1 [ID2 [KIAA1109 [TAGAP [SNX32, ZNF652, KLC2).Our findings also revealed novel trait associations. For example, eosinophil percentage and eczema colocalized more often than predicted based on their genetic correlation Fig. d. These ETS1 , 65 and KIAA1109 –68 and T9 [TAGAP ). These P = 0.002), although the association did not reach statistical significance for weighted median or MR-Egger methods  and the instrumental variable was not subject to weak instrument bias . This site has been linked to autoimmunity and Crohn’s disease phenotypes, but not ulcerative colitis [PTPN22 Cys1858Thr mutation.Variation in rs2476601 causes a missense mutation in  colitis . Immune  colitis . We idenZC3HC1/NIPA  variation has previously been linked to altered risk of coronary artery disease, but understanding of genes underlying this association is lacking . We idengulation . FurtherHigh FE2+  [HFE deficiency itself. Our data show that individuals heterozygous for the Cys282Tyr allele have lower reticulocyte count and higher total cholesterol and low density lipoprotein levels  . Patientoverload .TRAIL) is a promising novel chemotherapeutic target [TRAIL 3′ UTR was recently associated with decreased triglyceride levels [TRAIL.Finally, we hypothesized that our analysis might also help predict off-target effects of novel therapeutic agents. For example, tumor necrosis factor (TNF)-related apoptosis inducing ligand . We anticipate an expanded array of such targets could be revealed with larger, trans-ethnic GWAS.In an extensive genetic colocalization analysis, we have identified loci, genes and related pathways related to hematopoietic development. Further, our colocalization results identified loci relating 70 hematopoietic, cardiovascular, autoimmune, neuropsychiatric and cancer phenotypes. This repository of associations will be useful for mechanistic studies aimed at understanding biological links between phenotypes, for developing novel therapeutic strategies, and for predicting off-target effects of small molecule and gene therapies.Additional file 1: Table S1. Genome wide association study summary statistics used in our analysis. The trait(s) queried are shown, along with study Pubmed identification number (PMID). UK Biobank studies can be found using the link provided. Total European sample sizes, including cases and controls where appropriate, are shown for each study. PMIDs for data downloaded from the NHGRI-EBI GWAS Catalog can be found at the bottom of this table. Table S2. Traits and SNPs identified by colocalization analysis [Table S3. Traits and SNPs identified by colocalization analysis [g) < 0.8. All identified sites are shown in this table. Candidate SNPs are indicated as chr:pos. The posterior probability of colocalization, regional (genomic) probability of colocalization, and posterior probability explained at each locus are indicated. Table S4. ‘Hematopoietic stem cell’ sites at which white blood cell (wbc), red blood cell (rbc), and platelet (plt) counts colocalize. Sites were specified by chromosome and position. The rsID(s) associated with each site are shown. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S5. ‘RBC trait only’ sites at which only the indicated red blood cell traits colocalized, excluding platelet or white blood cell traits. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Rbc, red blood cell count. Hct, hematocrit. Mcv, mean red cell corpuscular volume. Rdw, red cell distribution width. Table S6. Gene ontology pathway analysis of genes regulated by eQTLs linked to ‘RBC trait only’ sites. Shown are pathways with p < 0.05 by Binomial test using Bonferroni correction for multiple testing. Table S7. ‘Platelet trait only’ sites at which only the indicated platelet traits colocalized, excluding red blood cell or white blood cell traits. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Plt, platelet count. Pct, platelet-crit. Mpv, mean platelet volume. Pdw, platelet distribution width. Table S8. Gene ontology pathway analysis of genes regulated by eQTLs linked to ‘platelet trait only’ sites. Shown are pathways with p < 0.05 by Binomial test using Bonferroni correction for multiple testing. Table S9. Traits and SNPs identified by colocalization analysis [Table S10. Traits and SNPs identified by colocalization analysis [g) < 0.8. All identified sites are shown in this table. Candidate SNPs are indicated as chr:pos. The posterior probability of colocalization, regional (genomic) probability of colocalization, and posterior probability explained at each locus are indicated. Table S11. Colocalization sites for lymphocyte count (lymph) and Asthma. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S12. Colocalization sites for neutrophil count (neut) and Asthma. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S13. Colocalization sites for eosinophil percentage of white blood cells (eo%) and Asthma. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S14. Colocalization sites for monocyte count (mono) and Asthma. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S15. Colocalization sites for mean platelet volume (mpv) and coronary artery disease (cad). Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S16. Colocalization sites for reticulocyte count (ret) and coronary artery disease (cad). Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S17. Colocalization sites for lymphocyte count (lymph) and coronary artery disease (cad). Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S18. Colocalization sites for eosinophil percentage of white blood cells (eo%) and Eczema. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S19. Mendelian randomization analysis results for the indicated exposure and outcome traits. Outcomes reflect increased risk of eczema (odds ratio) or depressive symptoms (in standard deviation units) per 1 standard deviation increase in exposure by inverse variance weighted, weighted median, and MR-Egger methods. Factors used to calculate genetic variance explained  on Eczema. The rsID (hg19), chromosome, position, effect allele, other (non-effect) allele, effect sizes and standard errors are shown for each SNP. Table S21. Colocalization sites for red blood cell count (rbc), basophil cell count (baso) and depressive symptoms (DepSx). Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S22. Instrumental variable data for MR experiments estimating effects of red blood cell count (rbc) on depressive symptoms (DepSx). The rsID (hg19), chromosome, position, effect allele, other (non-effect) allele, effect sizes and standard errors are shown for each SNP. Table S23. Instrumental variable data for MR experiments estimating effects of basophil cell count (baso) on depressive symptoms (DepSx). The rsID (hg19), chromosome, position, effect allele, other (non-effect) allele, effect sizes and standard errors are shown for each SNP. Table S24. Colocalization sites for breast cancer and schizophrenia. Gene symbols for the nearest gene, all eQTLs, all eQTLs in whole blood, and all sQTLs (sQTLseekeR and Altrans methods) are shown in the indicated columns related to the indicated SNP (rsID) and any SNPs in high linkage disequilibrium (r2 > 0.9). Table S25. Analysis of a coding variant (rs2476601) that causes a missense mutation in PTPN22 shows significant colocalization with white blood cell (wbc) count and Crohn’s disease. The effect sizes and direction (+/−) are shown. Table S26. Colocalization analysis for a coding variant (rs1800562) in HFE, mutations in which cause hereditary hemochromatosis. Effects on total cholesterol (TC), low density lipoprotein (LDL), and red blood cell traits , with significant colocalization signal at this locus, are shown. Table S27. Colocalization analysis for a coding variant (rs17600346) in Tumor necrosis factor (TNF)-related apoptosis inducing ligand , based on targeted analysis of the 50 kb region surrounding this site. Effects on colocalized white blood cell  and platelet traits  are shown.analysis  of 34 heanalysis  of 17 heined (R2 ) and insined (R2 ) are shoined  < 0.8. a Number of traits identified at each colocalization site (max = 12). b Heat map depicting percent overlap at colocalization sites between each hematopoietic trait pair. In each box, the number of sites where the row-specified trait and column-specified trait colocalized was normalized to the total number of colocalization sites for the ‘row trait’. For this reason, the heat map is asymmetric. Color scale represents the proportion of loci where each pair of traits colocalized. To the left of the heat map, hierarchical clustering accurately segregated red cell, platelet, and white cell traits in general agreement with blood lineage relationships. c Degree of colocalization (% overlap) generally reflects genetic correlation between trait pairs. Shaded area depicts the 95% prediction interval, with gray line at mean. Colored spots highlight trait pairs outside the 95% prediction interval that included 2 platelet traits (purple) or 2 red blood cell traits (red). Exemplary trait pairs are labeled. Plt, platelet count. Mpv, mean platelet volume. Pdw, platelet distribution width. Rdw, red blood cell distribution width. Mchc, mean corpuscular hemoglobin content. Mcv, mean red cell corpuscular volume. Figure S2. Genetic colocalization among traits with limited genetic correlation reveals shared regulatory loci and implicates causal genes underlying genetic associations between hematopoietic traits and disease end-points. The 45 traits analyzed were pruned for genetic correlation (rg) < 0.8. a Number of traits identified at each colocalization site (max = 14). b Heat map depicting percent overlap at colocalization sites between each trait pair. In each box, the number of sites where the row-specified trait and column-specified trait colocalized was normalized to the total number of colocalization sites for the ‘row trait’. For this reason, the heat map is asymmetric. c Hierarchical clustering based on colocalization results associates related traits, which are color coded according to the key in part b. d Degree of colocalization (% overlap) reflects genetic correlation between trait pairs. Shaded area depicts the 95% prediction interval, with gray line at mean. Exemplary trait pairs are circled. Depsx, depressive symptoms. Rbc, red blood cell count. Baso, basophil cell count. Brca, breast cancer. Scz, schizophrenia. eo%, eosinophil percentage of white blood cells.


The results were robust in sensitivity analyses and no evidences of horizontal pleiotropy were observed. While we found no significant associations between HBC traits and CPs. Our findings suggested eosinophils might play important roles in the pathogenesis of EPs. Besides, out study provided novel insight into detecting uterine polyps biomarkers using genetic epidemiology approaches.Human blood cells (HBCs) play essential roles in multiple biological processes but their roles in development of uterine polyps are unknown. Here we implemented a Mendelian randomization (MR) analysis to investigate the effects of 36 HBC traits on endometrial polyps (EPs) and cervical polyps (CPs). The random-effect inverse-variance weighted method was adopted as standard MR analysis and three additional MR methods  were used for sensitivity analyses. Genetic instruments of HBC traits was extracted from a large genome-wide association study of 173,480 individuals, while data for EPs and CPs were obtained from the UK Biobank. All samples were Europeans. Using genetic variants as instrumental variables, our study found that both eosinophil count (OR 0.85, 95% CI 0.79–0.93, P = 1.06 × 10 These structures are categorized based on their size, number, location, and presence/absence of a stalk. Endometrial polyps (EPs) are the most commonly diagnosed type of uterine polyps, with an estimated prevalence ranging from 7.8 to 50%, while cervical polyps (CPs) are the second most common 4. The polyps are usually asymptomatic but may cause a number of problems, such as abnormal uterine bleeding, subfertility, and risk of malignancy9. A number of etiological theories have suggested an association between the pathogenesis of EPs and CPs and various factors, including estrogen overstimulation, chronic inflammation, and genetic predisposition11. However, early biomarkers for informing diagnosis and identifying pathological mechanisms are still lacking.Polyps are frequently observed pathological growths in the uterus that occur in women during of both reproductive and postmenopausal age16. Changes in HBC traits may indicate disturbances in physiological processes and are associated with a series of pathological structural abnormalities, such as cellular degeneration, tissue proliferation, and even tumor formation18. In fact, chronic inflammation is a known etiological factor for both EPs and CPs, and histopathological examinations have shown that polyps are consistently accompanied by inflammatory infiltration5. This indicates that HBCs might participate in important biological processes during the development of uterine polyps. However, the associations between HBC traits and uterine polyps have not been investigated.Human blood cells (HBCs) play essential roles in oxygen transport, hemostasis, osmotic regulation, and clearance of necrotic tissue and toxins, and are involved in multiple inflammatory and immune responses of the human body19. The fundamental principle utilized in the MR design is that if genetic variants could predict a certain proportion of variance for a modifiable exposure, then they should be also causally associated with an exposure-related disease risk. MR presents a number of advantages over traditional observational studies, including the ability to prevent confounding, reverse causation, and various biases that are common in observational epidemiological studies20. Recently, the explosion in publicly available summary statistics of genome-wide association studies (GWASs) has provided extensive resources for the application of MR21. Here, by extracting summary data from a GWAS of HBC traits and uterine polyps, our study aims to provide an unbiased investigation of the effects of HBC traits on EPs and CPs.Mendelian randomization (MR) is a novel study design that uses genetic variants as instrumental variables (IVs) to investigate the relationship between risk factors and clinical outcomes of interestAfter harmonizing the alleles and effects between single nucleotide polymorphisms (SNP) associations with blood cell traits and GWAS datasets of outcomes, we obtained 46–246 genome-wide SNPs for the 36 HBC traits Fig. , 2. On a–4), the random-effect inverse-variance weighted (IVW) method identified four HBC traits that showed significant associations with EPs, including the eosinophil percentage of white cells , eosinophil count , sum eosinophil basophil counts , and neutrophil percentage of granulocytes . All four traits were granulocyte-related, and three were related to eosinophils in particular. Notably, the three eosinophil-related traits were associated with a decreased risk of EPs (the ORs range from 0.84 to 0.85), while the NEUT%GRAN was associated with an increased risk of EPs. However, we found no evidence for associations between blood cell traits and CPs.Primary results of MR estimates are presented in Fig. Pintercept = 0.787 for EO%, Pintercept = 0.774 for EO#, Pintercept = 0.513 for [EO + BASO]#, and Pintercept = 0.433 for NEUT%GRAN), indicating robust relationships between the four granulocyte-related traits and EPs (Table 2 > 25% or Cochran Q-derived P < 0.1). To control for heterogeneity in these MR estimates, we further performed outliers-corrected MR analyses by removing weak or pleiotropic instruments detected by Cochran’s Q tests. After removing the identified outliers, the effects of the four HBC traits on EPs were still robust .Sensitivity analyses showed consistent results with the primary random-effect IVW estimates for all four HBC traits Table , and no IVW = 3.38 × 10−5), BASO# , MONO# , and LYMPH#  and the effect estimates were consistent with initial MR analysis  across the three studies Table . SimilarOur study provided valuable information for screening novel biomarkers and understanding the pathophysiological mechanisms of EPs and CPs. We identified three eosinophil-related properties that were robustly associated with EPs, suggesting that eosinophils might play important roles in the pathogenesis of EPs. While we found no significant associations between HBC traits and CPs.22. Activated eosinophils release a series of proteins, cytokines, chemokines, and lipid mediators that participate in multiple biological processes such as endothelial proliferation, cell migration, mucus secretion, activation of vascular permeability, and regulation of mucosal homeostasis25. Eosinophils are widely observed in the endometrial stroma, the luminal and glandular epithelium, and the endometrial-myometrial junction of female genital tracts. However, their biological roles are still not well understood. A previous study reported that the presence of eosinophils in endometrial biopsies might indicate chronic endometritis, as well as disordered proliferative endometrium and EPs26. Another study suggested that the IL-4 released by eosinophils can promote endometrial stromal cell proliferation and repair genital tissue after infection27. Furthermore, elevated eosinophil counts are also frequently observed in patients with nasal polyps, suggesting that eosinophilic inflammation might cause specific mucosal polyps31. Our study provided some different evidence that higher level of eosinophils had a protective effect on EPs. While up to now, there was no clear evidence regarding the effect of eosinophils on EPs. The results could also vary depending on the source of tissue in the sample being measured. Anyway, our study together with previous studies indicated that eosinophils might be involved in EP pathogenesis.The associations between eosinophil properties and EPs had not yet been reported prior to this study. Eosinophils are multifunctional granulocytes involved in the pathogenesis of diverse inflammatory processes, including parasitic infections and allergic reactionsF statistic > 10) used to detect the relationships between HBCs and uterine polyps. All generated IVs were strong instruments for MR analyses.Our study had several strengths. First, the MR study design not only provided evidences for causal relationships between HBCs and uterine polyps, but also prevented the widespread bias that is common in observational epidemiological studies. Second, the datasets for HBCs and outcomes were all generated from a European population, which avoided the potential bias that might be caused by differences in genetic backgrounds. Third, the large sample size of the GWAS on HBCs guaranteed the strength of the IVs 34, and 40,521 individuals form the INTERVAL35. HBC traits were measured using clinical hematology analyzers at the centralized processing laboratory of the UK Biocenter . Genotyping was performed on the Affymetrix GeneTitan Multi-Channel (MC) Instrument according to the Affymetrix axiom 2.0 assay Automated Workflow. Detailed information for genotype imputation, quality control, and association analysis can be found in a previously published study32. Finally, a total of 6736 conditionally independent trait-variant pairs  with significance level at P < 8.31 × 10−9  were identified to compose IVs for the 36 HBC traits. The identified IVs were further mapped to the GWAS datasets of outcomes and SNPs were dropped while not available in datasets of outcomes. The strength of the IVs were evaluated by tow parameters: the proportion of variance explained (R2), which was calculated using the formula 2× MAF × (1 − MAF) × (β estimate in SD units)2, and the F statistic, which could be calculated from the R2 statistic as F = (N – K − 1)/K × R2/(1 − R2), where N is the sample size and K is the number of SNPs36. Typically, a threshold of F > 10 is recommended for defining instrument strength in an MR analysis37.The overall flow diagram of this MR study is illustrated in Fig. http://pheweb.sph.umich.edu/UKBiobank)38. The total study sample comprised 7910 EP cases and 3450 CP cases, as well as 396,384 shared heathy controls from the UK Biobank. Cases were diagnosed according to the International Classification of Disease (ICD) codes. Association analyses were performed using SAIGE (https://github.com/weizhouUMICH/SAIGE/), adjusting for genetic relationship, date of birth, as well as the first four principal components. All the data were extracted from the public domain and thus no ethical approval was required for this study.Genetic associations with EPs and CPs were obtained from the Michigan PheWeb v1.1.17  occurs39. To control for widespread horizontal pleiotropy in MR analyses, we further performed three additional MR analyses to serve as sensitivity analyses . MR-Egger provides consistent estimates even with invalid instruments under the Instrument Strength Independent of Direct Effect (InSIDE) assumption39. The weighted median introduces a median-based estimator which tolerated up to 50% of the IVs to be invalid, and provides a consistent estimate of causal relationships40. MR-PRESSO is a newly developed method that aims to control for horizontal pleiotropy by detecting and correcting for outliers41. We also tested for heterogeneity which could indicate pleiotropic instruments effects using the with the I2 and Cochran Q statistic, and an I2 > 25% or Cochran Q-derived p < 0.1 was adopted to declare evidence of heterogeneity42. Weak or pleiotropic instruments were detected according to the individual components of Q statistic and a corrected model were performed without these outliers43. Multivariable MR analyses were performed by using the random-effect IVW method to adjust for the effect of overlapped instruments with other blood traits. Sub-study MR analyses were also performed to avoid potential bias that might be introduced by sample overlapping, using effect size of IVs respectively from UK Biobank, UK BiLEVE and INTERVAL study cohorts44.MR analyses were performed using the random-effect IVW method. Briefly, the IVW approach makes the fundamental assumption that all included genetic variants are valid IVs. This requires each genetic variant to satisfy three conditions: (i) it is strongly associated with the exposure, (ii) it cannot be associated with any confounders, and (iii) it is associated with the outcome exclusively through the exposurewww.cran.r-project.org). A multiple-testing-adjusted threshold of P < 6.94 × 10–4  was defined as the threshold for declaring statistical significance.All MR analyses were carried out using TwoSampleMR and MVMR packages in R (The GWAS summary statistics for all traits were extracted from the public domain. Therefore, no ethical approval and consent was required for this study.Supplementary Table 1.Supplementary Table 2.


The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic Prediction (STMGP), was applied to improve the genome-based prediction of psychiatric phenotypes by decreasing overfitting through selecting variants and building a penalized regression model. Prediction models were trained using a cohort of 3685 subjects in Miyagi prefecture and validated with an independently recruited cohort of 3048 subjects in Iwate prefecture in Japan. Genotyping was performed using HumanOmniExpressExome BeadChip Arrays. We used the target phenotype of depressive symptoms and simulated phenotypes with varying complexity and various effect-size distributions of risk alleles. The prediction accuracy and the degree of overfitting of STMGP were compared with those of state-of-the-art models . In the prediction of depressive symptoms, compared with the other models, STMGP showed the highest prediction accuracy with the lowest degree of overfitting, although there was no significant difference in prediction accuracy. Simulation studies suggested that STMGP has a better prediction accuracy for moderately polygenic phenotypes. Our investigations suggest the potential usefulness of STMGP for predicting polygenic psychiatric conditions while avoiding overfitting. In a polygenic model, building high-performance prediction models based on GWAS in training data is challenging because selecting only truly (weakly) associated variants based on a single GWAS with the currently available largest sample size is difficult due to limited statistical power  revealed that the genetic influences on many psychiatric conditions are based on the aggregation of a large number of small effects, which is referred to as a polygenic modelwer Fig. 4,5. The 7, is calculated as a sum of the number of trait-associated alleles, defined based on an arbitrary P-value cutoff multiplied by their regression coefficients  is limitedly considered for prediction. Although limitations of statistical power need to be solved by a larger sample size, the effective utilization of the SNP group in LDs could be considered by building a multiple-regression model. In addition, the overfitting from null variants of PRS could be decreased to some extent by building a penalized regression model.There have been two frequently utilized prediction methods for complex diseases following a polygenic model: polygenic risk scores (PRS) and genomic best linear-unbiased prediction (GBLUP). PRS, proposed by Purcell et al.nts Fig. . Althoug13. Unlike PRS, GBLUP can utilize a group of variants correlated with each other effectively for prediction. However, the prediction accuracy of GBLUP is also low14. One of the limitations of GBLUP models is that the method does not select specific variants, and includes a large number of null variants, which could decrease prediction accuracies through overfitting, as observed in PRS.Another frequently utilized genomic prediction method for complex traits is GBLUP, which fits all the variants simultaneously by building linear mixed models, treating the effects of the variants as random effects Fig. 11–13. Un15. Similar to PRS, STMGP also builds prediction models based on variants selected by the threshold P value of GWAS. However, STMGP overcomes the problems of PRS in the following two aspects. First, STMGP can avoid overfitting by weighting variants by the strength of marginal association reflecting the certainty of inclusion, which increases and stabilizes prediction accuracies16. Second, in STMGP, all the selected variants are utilized as predictor variables to build a penalized regression model, generalized ridge regression, which enables the effective utilization of correlated susceptibility variants for better prediction accuracy  was proposed by Ueki and Tamiyaacy Fig. . The STM15, we hypothesized that STMGP would have good performance in predicting polygenic psychiatric phenotypes. In this study, we first evaluated the performance of STMGP in predicting depressive symptoms based on real GWAS data, including 3685 training and 3048 validation cohorts. Depressive symptoms are one of the most polygenic phenotypes according to the previous GWAS20, whose SNP-based heritability has been estimated to be 0.04 (SE 0.01)21. Then, we also evaluated the performance of STMGP using simulated phenotypes with varying degrees of complexity and various effect-size distributions. The performance of STMGP was evaluated in terms of prediction accuracy and the degree of overfitting, and compared with that of other state-of-the-art methods, which included, in addition to PRS and GBLUP, summary-data-based best linear-unbiased prediction (SBLUP)22, BayesR 23, and ridge regression  using clumped SNP data.Considering the performance of STMGP previously reported by Ueki and Tamiya25. Both cohorts were collected in an effort to survey the health condition of residents in the prefectures affected by the 2011 Great East Japan Earthquake and Tsunami. HumanOmniExpressExome BeadChip Array  was utilized for genotyping for both cohorts. Subjects with a low call rate  were excluded. We detected 2156 close-relationship pairs  using the identity-by-descent method in PLINK software (PI_HAT > 0.09375)26 among the training cohort, the validation cohort, or between these cohorts. Then, in each of these pairs, a subject with lower call rates was excluded. Variants with low call rates (<0.99), low Hardy–Weinberg equilibrium exact-test P values (<1 × 10−4), or low minor-allele frequencies (<0.01) were filtered out. Subjects without outcome or covariate information  were excluded. Finally, 3685 subjects in the training cohort and 3048 subjects in the validation cohort with 615,386 variants were subjected to prediction analyses. The imputed genome datasets were used in the additional analyses, and the imputation method is shown in the Supplementary Methods. All protocols of our studies were approved by the Ethics Committees of Tohoku University and Iwate Medical University. Written informed consent was obtained from all subjects. This study was carried out according to the principles expressed in the Declaration of Helsinki.The genome-wide SNP data for a total of 9966 subjects, including 4974 training cohort subjects living in Miyagi prefecture recruited by Tohoku University and 4992 validation cohort subjects living in Iwate prefecture recruited by Iwate Medical University, were available at the time of the current analysis27. The CES-D contains 20 items, each of which is rated on a 4-point scale ranging from 0 to 3 based on the frequency of feelings and behaviors over the past 7 days, with a higher score suggesting a severe depressive state. There were 2.0% and 0.7% missing data in CES-D items in the training and validation cohorts, respectively, and listwise deletion was performed for missing data.Depressive state was evaluated by the Center for Epidemiological Studies-Depression (CES-D) score28. The samples with CES-D scores of 0 or greater than 33  were determined to be outliers and excluded in this additional analysis.The distributions of CES-D scores in the current datasets are shown in Supplementary Fig. The demographics of the members of each dataset are shown in Table 22, and compared the prediction accuracy of STMGP and those of other prediction models. We set the number of true variants at 100, 200, 500, 2000, and 5000. We set the effect-size distribution for susceptibility SNPs by the normal and Laplace distribution30. We also considered the normal–exponential gamma (NEG) distribution based on previous studies33. The NEG distribution can be modeled as a Laplace distribution with a gamma-distributed rate, and it has thicker tails than both the normal and Laplace distributions. Decreasing the gamma-shape parameter leads to thicker tails, whereas increasing this parameter restores the distribution to the Laplace distribution. We set the shape parameter at 2, 3, and 10. In each analysis, 20 replications were performed, and the mean predictive correlation coefficients (PCCs), standard deviations, and P values for the PCCs were calculated.We prepared simulated phenotypes based on our actual SNP data following the previous studyr2 < 0.05). Then, we simulated a phenotype across all individuals, including the training and test datasets, with the selected SNPs as follows:To prepare the phenotype, we randomly selected the above-mentioned number of SNPs that were in approximate linkage equilibrium  of the jth sample. Furthermore, bi was simulated from the Laplace, normal, or NEG distribution with mean = 0 and variance = 1, and ej was simulated from a normal distribution with mean = 0 and variance = h2 is the heritability of the trait. h2 was set at 0.05 and 0.10, based on the SNP heritability of CES-D in the current analysis (0.05) and the SNP heritability in the previous large GWAS for depressive phenotype (0.047–0.102)20. Similar to the analysis regarding the CES-D scores, the prediction models were built based on the 3685 training samples and evaluated on 3048 independent validation samples.34, which tests the difference between two dependent correlations sharing one variable, implemented in the psych package of R.We estimated the partial correlation coefficients controlling for covariates  in the models for the prediction of depressive symptoms. The PCC was used for the prediction of simulated phenotypes. To test the significance of the difference between the partial correlation of STMGP and those of the tested methods, we used William’s test35. For the inputs of stmgplink function, we prepared SNP data, phenotype data, and covariate data for both the training and test datasets, as well as two tuning parameters, τ and γ. STMGP requires individual-level SNP data, not summary statistics, for calculating the correlation between SNPs. The stmgplink function sequentially (1) computed GWAS P values, (2) identified an optimal P-value cutoff using Mallows’ Cp criterion, (3) built the prediction model from the STMGP model  of the training dataset, and (4) predicted the phenotypes in the test dataset15.The program code for STMGP (STMGP v1.0), including the stmgplink function, was available via CRAN, the official R package archiveP-value cutoff, there are two tuning parameters for STMGP, τ and γ. τ controls the extent of the overall penalization. Specifically, τ regulates the loss function ||y−XAβA||2, where y, XA, and βA indicate the vector of the phenotype, the matrix of the predictor variables , and the vector of the regression coefficients. A denotes the set of selected SNPs  at a given P-value cutoff. Thus, τ must be adjusted depending on the sample size (N) because the loss function is the residual sum of squares, and thus increases proportionally to N when N is large. The previous study by Ueki and Tamiya15 suggested τ equal to τ was set to N/0.1, N/1, and N/10 were also performed.In addition to the γ controls the influence of the input GWAS test statistics on the SNP weight for the generalized ridge-regression model, and the magnitude of the P value is incorporated by smooth thresholding rather than the hard thresholding utilized in PRS. Because of the equivalence to the γ parameter in the adaptive Lasso36 shown in ref. 16, we set γ to the commonly used value 1 for the adaptive Lasso used herein40.Packages and parameters used for genetic predictions other than STMGP are shown in the Supplementary Methods section.P values for all the principal components  were calculated based on the Tracy–Widom distribution using the Eigensoft package42. The components with P values < 0.05  were used as covariates. The SNP data, including the training and test datasets, were used for calculating the principal components, to ensure that the same set of principal components was used for the training and test datasets. The scatterplot generated by principal component analysis is shown in Supplementary Fig. b0, b1, b2, b3, b4, … bSNP) were obtained by the STMGP method, and the values were used to calculate the predicted score of each test sample by the following formula:y, AGE, and SEX represent an individual’s phenotypic score (CES-D), age, and sex, and PC1, PC2, … are the principal components that must be adjusted for. etrain is an error term. SNP represents the vector of the standardized allele number of the selected SNPs. Covariate adjustment for genetic predictions other than STMGP is shown in the Supplementary Methods section.The covariates of sex, age, and the significant principal components to control population stratification were included in the prediction models. The principal component analyses were performed for the SNP data, including the training and test data (6733 subjects), and the 45. The damage was coded based on house damage as determined by the local government43 following the national damage certification standards of disasters: 4 = total collapse (uninhabitable), 3 = large-scale damage (requires major repairs), 2 = half-scale damage (habitable with repairs), 1 = small-scale damage, and 0 = no damage.Because the method for adjusting for confounding covariates can affect the prediction accuracy, we investigated the consistency among the results under different methods of handling the covariates. We considered three additional approaches in addition to calculating the partial correlation coefficient adjusted for age, sex, and principal components. In the first approach, we regressed out age and sex, and evaluated the prediction accuracy of our genetic scores against the residuals of the regression. In the second approach, we calculated the predictive correlation coefficient without adjusting for the covariates. The third approach was the inclusion of the degree of damage from the 2011 Great East Japan Earthquake and Tsunami into the covariates, because depressive phenotypes may have been affected by this event3. This genome-wide meta-analysis was one of the largest meta-analyses related to depression, and included 170,756 cases and 329,443 controls, from 33 cohorts of the Psychiatric Genomics Consortium as described in Wray et al.2 and the broad depression phenotype in the full release of the UK Biobank as described in Howard et al.20.To investigate the relationship between the SNPs selected by STMGP and the risk alleles suggested by previous GWAS studies, we referred to the results from the genome-wide meta-analysis by Howard et al.47 within a 100-kilobase window around the 102 SNPs selected by STMGP using proxysnps software (https://github.com/slowkow/proxysnps). Then, we calculated r2 values between these proxy SNPs and the SNPs included in STMGP, and we checked the P values of the proxy SNPs in the previous genome-wide meta-analysis3. To evaluate the MAFs of the SNPs used in the STMGP algorithm among different ethnic groups, the Genome Aggregation Database (gnomAD)48 was utilized.We first selected the proxy SNPs from phase 3 of the 1000 Genome ProjectP values > 0.05). When the training cohort was used both for building the model and calculating the prediction accuracy, i.e., the apparent (resubstitution) prediction accuracy, the partial correlation of STMGP was less optimistic than that of the other models, and the degree of overfitting successfully decreased in STMGP  with the SNPs selected by STMGP, the smallest P value was 0.00112 (rs2678198). The highest r2 value between the SNPs selected by STMGP and the risk alleles suggested by the previous meta-analysis  was 0.0722 (rs4977974 and rs1758737). Information about ethnicity for the selected 102 SNPs is shown in Supplementary Fig. Information about the variants used for STMGP and the related information from a previously published genome-wide meta-analysisrong LD r > 0.8 wiP-value cutoff obtained by STMGP was 2.7 × 10−4, which was lower than the P-value cutoff obtained by PRS (0.022). We also analyzed PRS using the same cutoff of that used for STMGP, and the partial correlation of this modified PRS was 0.0230, which was not higher than that of the original PRS (0.0247).The 23.The slope of the regression of the phenotype (CES-D) on predicted values was calculated to compare the difference in the predicted score and the difference in the phenotype. The slope values of the regressions (SE) for STMGP, PRS, GBLUP, SBLUP, BayesR, and ridge regression were 0.591 (0.137), 0.100 (0.055), 0.097 (0.055), 0.063 (0.057), 0.050 (0.055), and 0.050 (0.033), respectively. The slope of the regression and scatterplots of the true and predicted scores are shown in Supplementary Fig. τ parameters are shown in Supplementary Table τ values, and setting τ = N/10 showed the best partial correlation (0.0964) in the current datasets. The performance of the models predicting the phenotype with different distribution (Box–Cox-transformed phenotype/the outlier-excluded dataset) is shown in Supplementary Table P value > 0.05), STMGP showed better prediction accuracy than the other methods by decreasing overfitting.The prediction accuracies of STMGP using different ion 0.096 in the cFollowing the prediction analyses for depressive symptoms, studies using simulated phenotypes based on the current SNP data were performed to evaluate the performance of STMGP in predicting phenotypes with varying complexities and various effect-size distributions of risk SNPs. The results of the simulation studies  are shown in Tables The current study showed that STMGP is useful for predicting psychiatric polygenic phenotypes. In predicting depressive phenotypes, STMGP showed the highest prediction accuracy, and in the simulation study, STMGP tended to have better performance in predicting moderately polygenic phenotypes. The strategy of STMGP  successfully reduced overfitting. The computational cost of STMGP was relatively low for our imputed genome data.P value < 5 × 10−8), with only 11 variants with P value < 5 × 10−5. The top 11 variants explained only 3.6 × 10−3% of the variance in the target phenotype in the validation dataset. This small effect size of the susceptibility variants and difficulty in genetic prediction for depression were consistent with previous findings14, which may confirm that depression is one of the most difficult target diseases for genetic prediction. Even in such a challenging situation, STMGP successfully avoided overfitting and yielded the highest prediction accuracy.The prediction for depressive symptoms in the current datasets was a challenging situation, with low prediction correlations for all state-of-the-art methods, in which only STMGP showed significant prediction accuracy, but the difference in accuracy between STMGP and the other models was not significant. The largest standardized regression coefficient of the susceptibility variants, which was calculated by PLINK using the standard beta option, for the depressive phenotype in the current study, was only 0.057. The GWAS results in the training dataset of the current study yielded no genome-wide significant variants , which could be due to the following reasons. First, the heritability was set to a low value (0.05 or 0.10) in the simulation study, and the effect size of each risk SNP was so small in the highly complex phenotypes that any prediction model, including STMGP, could not effectively use the genetic effects for prediction. Second, the strategy of STMGP to screen SNPs and to adaptively penalize the regression coefficients of each SNP depending on the effect size would be more effective in predicting phenotypes in which the SNP effects have a heavy-tailed distribution than a normal distribution. Considering the finding that STMGP tends to have better performance in moderately polygenic scenarios, it is possible that STMGP could also have good prediction accuracy in predicting less polygenic psychiatric conditions than depression 14.When the slopes of the regression of the phenotype for the predicted values were evaluated, the slope of the regression (SE) with STMGP was 0.591 (0.137), and those with the other prediction models were even lower. These relatively low slope values would be due to low prediction accuracy rather than bias of the prediction models because the slope values calculated in the training datasets were close to 1 in all the models (0.959–1.032) , ethnicity , and a limited sample size. In fact, the MAFs of the selected SNP were substantially different between the East Asian and European populations. This study succeeded in showing superior performance of STMGP compared with the other prediction models in the current datasets in which all the participants were Japanese. To apply the STMGP model to a different dataset with different ethnicities, such as European and African samples, updates for the training data would be needed, i.e., including samples of close ethnicity to the target population.This study was intended to compare the STMGP algorithm with other prediction models, rather than to discuss the significance of the associations between depression and the individual SNPs selected by STMGP. Although the selected SNPs may have included SNPs that were associated with the CES-D in both independent cohorts in this study after controlling for population stratification, the 21. The higher SE in the current study relative to that in the previous meta-analyses could be due to the smaller sample size.The SNP heritability estimated by GREML based on 3,685 training samples was calculated to be 0.05 (SE 0.07) in this study. This heritability value was consistent with the result of one of the largest genome-wide association meta-analyses using 70,017 subjects and depression scores (SNP heritability of 0.04 (SE = 0.01))−3% (PCC = 8.13 × 10−3)50 if all marker effects are assumed to come from the same normal distribution. The STMGP showed better prediction accuracy than the above-mentioned theoretical prediction accuracy, which suggested that the generalized ridge-regression models would better fit the current genome and phenotype data than linear mixed models.The expected prediction accuracy could in theory  be calculated to be 6.61 × 10BayesR was developed with a similar rationale as STMGP, which tries to reduce the inclusion of noise, and refine the true association between the phenotype and SNPs by setting multiple mixture distributions of a point mass at zero and normal distribution with different variances. BayesR showed the second highest prediction accuracy in the simulation study based on SNPs only, while the prediction analyses for CES-D scores or the simulation study based on SNPs and covariates did not show the superior performance of BayesR compared with other prediction models. It is possible that our regression models to make use of covariates to increase the prediction accuracy of BayesR were not as effective as with other prediction models with covariate options in the package. Furthermore, it is possible that the sample size was relatively small for BayesR.There are several limitations related to the study design and the current STMGP implementation. For study design, this study builds and evaluates prediction models based on limited data. The generalization of the results and scalability of prediction models needs to be discussed in the context of larger samples of GWAS data in the future. The lack of information about interventions regarding depression is another limitation of the current study. Information about medication was available only for the discovery cohort at the time of the current analysis. In the discovery cohort, 56 1.5%) subjects were taking antidepressant drugs. The equivalent number of subjects in the validation cohort may have been taking antidepressant drugs because the prevalence of previous psychiatric history was similar between the discovery and validation cohorts, as shown in Table .5% subjeP-value cutoff. However, the current implementation is suitable for using a few thousand individuals for training data with an SNP array, imputed genome data, or whole-genome sequencing data. In addition, STMGP can currently handle only individual-level SNP data, not summary statistics, for calculating the correlation between SNPs. We are planning to improve the scalability and develop options for using summary statistics.There are two limitations to the current STMGP implementation. The STMGP algorithm is essentially a variant of the generalized linear model with an added weighted L2 penalty, meaning that the scalability is comparable with that of the linear/logistic regression with predictor variables screened by a In conclusion, this study showed the potential usefulness of STMGP in predicting polygenic psychiatric phenotypes using real GWAS- and simulated data. The strategy to reduce overfitting through screening and building penalized regression models was suggested to be effective in genetic prediction, especially for moderately polygenic phenotypes. Considering its predictive performance and lower computer costs compared with other penalized regression models, STMGP is recommended for the genetic prediction of psychiatric conditions with a polygenic model.Supplementary MethodsSupplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5Supplementary Figure 6Supplementary Table 1Supplementary Tables 2–7Supplementary Table 8Supplementary Table 9