OsRSL3 was found in a QTL region for median metaxylem diameter. Four SNPs in OsRSL3 were found that caused amino acid changes and significantly associated with the root phenotype. Based on the haplotype analysis for median metaxylem diameter, the rice accessions studied were classified into five allele combinations in order to identify the most favorable haplotypes. The candidate genes and favorable haplotypes provide information useful for the genetic improvement of root phenotypes under drought stress.Drought stress in Southeast Asia greatly affects rice production, and the rice root system plays a substantial role in avoiding drought stress. In this study, we examined the phenotypic and genetic correlations among root anatomical, morphological, and agronomic phenotypes over multiple field seasons. A set of >200 rice accessions from Southeast Asia (a subset of the 3000 Rice Genomes Project) was characterized with the aim to identify root morphological and anatomical phenotypes related to productivity under drought stress. Drought stress resulted in slight increases in the basal metaxylem and stele diameter of nodal roots. Although few direct correlations between root phenotypes and grain yield were identified, biomass was consistently positively correlated with crown root number and negatively correlated with stele diameter. The accessions with highest grain yield were characterized by higher crown root numbers and median metaxylem diameter and smaller stele diameter. Genome-wide association study (GWAS) revealed 162 and 210 significant SNPs associated with root phenotypes in the two seasons which resulted in identification of 59 candidate genes related to root development. The gene  As the major rice production area in the world, Southeast Asia relies on agriculture as the primary source of income, and climate change will have a significant impact on agricultural production. Since many rice planting areas in Southeast Asia rely on rain, drought is considered as a major problem in rice production in Southeast Asia. In the case of Northeast Thailand, rainfed lowland rice cultivation covers approximately 75% of the agricultural area in Northeast Thailand , and droDRO13.2qDTY , and their associated genetic regions, in a subset of the 3000 Rice Genomes  originatA set of >200 accessions from the 3000 Rice Genomes Project  were selA series of field experiments were conducted at the Zeigler Experimental Station of the International Rice Research Institute  to investigate root traits related to performance under drought Table 1.drought stress – top anglewell-watered.Root crowns (one hill per plot in transplanted experiments and one plant per plot in the 2018 direct-seeded experiment) were sampled at 48-52 das in the experiments using the full subset of accessions (2016WS and 2018WS), and at 76-82 das in the experiments on the smaller subsets (2017DS-2018DS). The root crowns were manually sampled with a spade to a depth of about 25 cm and a distance of 25 cm from the base of the plant, then gently washed with tap water. The number of crown  roots and the number of tillers were counted from each root crown. Each root crown from the 2016WS-2018DS experiments was then imaged together with a size standard and analyzed in DIRT  to deterdrought stress – percent deep rootswell-watered.Soil cores  were sampled half-way between rows from three locations per plot in the experiments on the smaller subsets at 77-82 das in the 2017DS, 2017WS, and 2018DS, and from one location per plot in the 2018WS experiment at 56 das using the full subset of accessions. Each soil core was divided into 15-cm depth segments, and roots were gently washed from the soil and scanned at 600 dpi (WinRhizo Pro v. 2013e) to determine root length and root length within diameter classes. We considered S-type lateral roots as those with a diameter of <0.05 mm, L-type lateral roots as those with a diameter of 0.05-0.2 mm, and nodal roots as those with a diameter >0.2 mm. The “percent deep roots” was calculated as the total length in the soil core below a depth of 30 cm divided by the total root length at all depths in the soil core, multiplied by 100. Percent deep root increase under drought was calculated as percent deep rootsRoot anatomical parameters were measured in all experiments using basal nodal root segments (about 1-4 cm from the root-shoot junction) from the root crown samples which were stored in 75% ethanol. Based on the infolding of the cortex in root samples from 2016WS, the nodal root segments in 2017DS, 2017WS, and 2018DS were prepared by critical point drying using an ethanol series incubation to remove the water. Since that approach did not appear to resolve the infolding, the root anatomy was again analyzed on roots stored in ethanol in 2018WS.2 until the pressure reached 50-70 kg cm-2. Inlet valves were closed, and the exhaust was opened until the pressure dropped to zero. This applying and releasing of pressure was done four times. Root samples were cut into 2-cm length segments, placed in a histocap and put into the chamber with a few drops of 100% ethanol. The chamber cap and all valves were closed. Temperature was set to -10°C, and then substitution with CO2 was performed by applying pressure at 50-70 kg cm-2 and gently opening the exhaust until no moisture was detected. The exhaust valve was closed, and pressure was applied again allowing the samples to sit for 30 minutes. Evaporation was done by setting the temperature to 37°C until the pressure increased up to 100 kg cm-2. Leak valves were opened to set the discharge rate. Before removing the samples, the temperature was set to room temperature.For the critical point drying, samples were incubated for one hour in 80%, 90%, and 100% ethanol, respectively. At 30 minutes before the one-hour incubation in 100% ethanol was completed, preliminary operation of the critical point dryer was performed to hasten the temperature drop. The temperature was set to -10°C, and then the chamber was filled with 50-80% COSamples were sent to PennState where basal root segments were sectioned, imaged, and analyzed with laser ablation tomography (LAT) as described in 2 (16 hills) in 2016WS, 1.5 m2 (30 hills) in 2017DS-2018DS, and 1 m2 in 2018WS was harvested from each plot. Straw biomass and grain yield  were determined from each plot. Harvest index was calculated as grain yield/(grain yield + straw biomass).At maturity, an area of 0.8 mRoot anatomical phenotype reduction was calculated on the lsmeans for each accession as To identify correlated phenotypes across the three experiments on smaller subsets based on maturity group (2017WS-2018DS), principal component regression was conducted according to 2) was determined on phenotypes measured in 2016WS and 2018WS using PBTools 1.4 (bbi.irri.org). To determine the most stable and highest-yielding accessions in terms of grain yield, an additive main effects and multiplicative interaction (AMMI) analysis  component that is ordered λ1≥λ2 ≥…≥λt; αik are elements of the kth left singular vector of the true interaction and represent genotypic sensitivity to hypothetical environmental factors represented by the kth right singular vector with elements γjk. The terms αik and γjk satisfy the constraints ’ and PTo examine inter-related phenotypes in the experiments using the full subset of accessions (2016WS and 2018WS), path coefficient and Pearson’s r correlation were analyzed by GenStat 19th Edition software  for all https://snp-seek.irri.org. A total of 1,011,601 SNPs were identified for the root phenotypes and agronomic phenotypes of the 210 rice accessions. Missing data of 10% or more were filtered out using PLINK. The resulting genotype data were further filtered using 5% minor allele frequency as the criterion. In the end, there were 462,202 SNPs that were used in determining the population structure that were estimated by PCA using the software TASSEL  enrichment analysis was carried out to further characterize the main molecular functions, biological processes, cellular components and protein classes of gene sets from each QTL region. GO enrichment analysis was performed using lantGSEA .From the GWAS results, linkage disequilibrium blocks and haplotypes of significantly associated SNPs were examined. Haploview software was implemented with the solid spline method for defining blocks in each chromosome . After b-2 in 2016 while the maximum biomass obtained under stress was 783 g m-2, and the grain yield was reduced to half in the drought stress treatment. In 2018, the biomass was reduced by more than half under drought stress, with the highest biomass amounting to 871 g m-2  was significantly negatively correlated with biomass in both seasons  and total crown root number (0.62) contributed negatively in PC1. Both grain yield (0.86) under stress and harvest index (0.81) had negative contributions in PC2, while crown roots per tiller had a positive contribution in PC3 (0.69) . Matching the genotype and phenotype data resulted in a set of 209 varieties comprised of mainly two groups of sub-populations: indica and non-indica as indicated by a phylogenetic tree (In order to identify genetic regions related to the agronomic and root phenotypes measured by genome-wide association studies (GWAS), we first examined linkage disequilibrium and population structure on the full set of accessions grown in 2016 and 2018. Chromosome 6, followed by chromosomes 4 and 11, had the highest reduction in linkage disequilibrium   and signOsRHL1), one SNP (T/C) at position 4192581 on chromosome 6, explaining 11.4% of the variation in total crown root number in 2016, was found to be significant and highly associated with the phenotype (OsRSL3) that are significantly associated with the phenotype. SNP positions 17349241, 17349289, 17349299 and 17349635 contributed 28.4%, 2.9%, 8.4% and 30.3%, respectively to phenotypic variation in median metaxylem diameter in this panel , with a functional SNP at position 1,434,806 for stele diameter in 2018 explaining 16.7% of the phenotypic variation was significantly associated with metaxylem diameter, number of metxylem, biomass, and crown root number in 2018. This is an example of a QTL identified by GWAS and identified as a functional SNP. The expression of OsDREB1C is induced by light and low-nitrogen levels. Overexpression lines of OsDREB1C (OsDREB1C-OE) exhibited higher grain yield by increasing the grain number per panicle  all likely affect this relationship. In this study focused on basal root anatomy of a Thai/Southeast Asian rice panel, the most consistent result across phenotypic and genetic approaches was a negative relationship between stele diameter and biomass and yield under drought. The current study emphasized the strength of a genetic analysis to provide insight on phenotypic relationships through SNP effects on various phenotypes. In addition to these implications for the phenotypic correlations, the favorable haplotypes identified may be further investigated for use in breeding. Our next step will be to characterize the frequency of these haplotypes in an elite pool of drought breeding lines, to assess if it would be beneficial to introgress them into an elite background for further testing and validation.https://dataverse.harvard.edu/dataverse/RiceResearch.The datasets presented in this study can be found online on the IRRI Dataverse page: JS and BT, formal analysis, visualization, writing – original draft preparation. MN, data curation, investigation, visualization. MQ, data curation, formal analysis, visualization. DC, methodology, writing – review & editing. KM, resources, writing – review & editing. JL, funding acquisition, investigation, project administration, writing – review & editing. KB, investigation, writing – review & editing. AH, conceptualization, funding acquisition, project administration, writing – original draft preparation. All authors contributed to the article and approved the submitted version.This work was funded by the BBSRC Newton Fund Grant BB/N013697/1, as well as the Bill and Melinda Gates Foundation project OPP1088843.We thank Michael Williams, Robert Snyder, and Hannah Schneider for assistance with the laser ablation and anatomical analysis at PSU. The University of the Philippines Los Baños kindly allowed use of their critical point dryer for this study. We thank Rolando Torres, Leo Holongbayan, Carlo Cabral, Eleanor Mico, Lesly Navarro, Ariston Reyes, and Philip Zambrano for support in conducting the field trials and analyzing the root samples at IRRI, as well as Rose Imee Zhella Morantte and Mathurada Ruangsiri for conducting statistical analysis and Marjorie De Ocampo for support of the GWAS analysis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N =  ~ 4700 twin pairs from the Netherlands Twin Register, us ing generalized estimating equations and twin-difference scores. When twin-difference scores indicated a role for genetic factors, we used bivariate genetic models to quantify genetic and environmental contributions to these associations. We identify significant associations between well-being and family functioning, family conflict, different leisure time activities, number of friends, and satisfaction with friendships. Additionally, we find evidence for large (73–91%) genetic influence on the associations between well-being and family conflict and functioning, leisure time sport/scouting clubs, and satisfaction with friendships. Finally, findings support the hypothesis of a causal association between well-being and family conflict and functioning. These findings have important implications for research into the social correlates of well-being in adolescence, as not taking genetic factors into account leads to overestimations of the influence of identified correlates and consequently to recommendations of these correlates as intervention targets.Socio-environmental factors play an important role in adolescent well-being, but potential genetic contributions to these associations are rarely assessed. To address this gap in the literature, associations between well-being and family conflict and functioning, number of friends, friendship importance and satisfaction, and leisure time variables were studied in The online version contains supplementary material available at 10.1007/s00787-021-01798-3. Adolescence, defined by the WHO as the period between age 10 and 19, marks a period in life where a person transitions from childhood to adulthood. During this transition period, the body rapidly develops, and there is accumulating evidence that adolescence is a critical period for later health and disease , so that all questions were scored in the same direction. After this transformation, items are summed to create a total family functioning score, with higher scores indicating higher levels of dysfunction. An example of an item is ‘planning family activities is difficult, because we misunderstand each other’.ce (FAD) . This 12Family conflict is assessed using a Dutch translation of the subscale Conflict of the Family Environment Scale (FES) [α = 0.0.73). An example of item is: “In our family we argue a lot”. One item (“We seldom get openly angry at each other at home”) is reverse-coded, so that answering yes implies low family conflict, whereas yes on the other items indicates high family conflict. The Dutch translation of this item was misinterpreted by a lot of participants, leading to inconsistent data patterns or missing data [le (FES) . The subing data . TherefoFriendship is assessed in three ways: 1) “How many good male/female friends do you have?”; 2) “In general, how satisfied are you with your female/male friends?”, and 3) “In general, how important are your female/male friends to you?”. To minimize the number of statistical tests, we summed the responses for male and female friends for (1), and took the mean for (2) and (3). For question 1, the participant could answer within the following categories: 0) I do not have any good friends; 1) 1 or 2; 2) 3 or 4; 3) 5 or 6; 4) 7 or 8; 5) 9 or 10; 6) 11 to 15; 7) more than 15. For questions (2) and (3), the answering categories were as follows: 0) very dissatisfied/unimportant; 1) dissatisfied/unimportant, 2) somewhat satisfied/ important; 3) satisfied/important; 5) very satisfied/important.Using the full sample  and their common environment (C), intra-pair difference scores between the twins must be the result of unique environmental experiences (E). On the other hand, intra-pair difference scores in DZ twins can be a result of differences in unique environmental influences (E), but also a result of differences in their genetic makeup (A), since they only share 50% of their genetic material on average. In a twin-difference design, intra-pair difference scores for one trait are regressed on the intra-pair difference scores of another trait. Based on these analyses, we expect the following given there is an observed phenotypic association between the traits see Fig. :Fig. 1ExI. If there is a significant association between the intra-pair difference score of well-being and the intra-pair difference score of a social variable in both MZ and DZ twins, this supports the hypothesis there is a causal relation between the two traits, or a large role for E. Genetic factors are additionally likely to contribute when the absolute DZ regression coefficient is larger than the absolute MZ regression coefficient.II. If there is a significant association between the intra-pair difference score of well-being and the intra-pair difference score of a social variable in DZ twins solely, it suggests that A plays a large role in the association, supporting the hypothesis that genetic factors act as a third unobserved variable underlying the association.III. If in both MZ and DZ twins, the intra-pair difference score regression returns non-significant results, the covariance between two traits is likely caused by C as these are 100% shared in both types of twin pairs.α = 0.05/13 = 0.0038 to correct for multiple testing .We calculated difference scores for all variables in all twin pairs by subtracting the score of one twin from the score of the other twin. Next, for each social variable, we regressed the social variable difference score on the well-being difference score using linear regression while correcting for a variable that reflects whether the twin pair is same sex or different sex. Since MZ twins are always same sex, we did not control for sex in MZ difference score analyses. We used a significance threshold of For associations where we find significant intra-pair difference regression results in both MZ and DZ twins or in DZ twin solely, we use bivariate genetic models to quantify genetic and environmental influences on the covariance between well-being and social variable. The difference in genetic relatedness between MZ and DZ twins enables decomposing the (co)variance of the traits under investigation into additive genetic (A), dominant genetic (D), common environmental (C), and unique environmental factors . Since C and D cannot simultaneously be estimated based on MZ and DZ covariance alone, either an ACE or ADE model is fit. When the MZ correlation is more than twice the DZ correlation, an ADE model is fit. When the MZ correlation is less than twice the DZ correlation, an ACE model is fit. Based on the literature, it was likely that the twin correlations for well-being might suggest an influence of D , while tTwin correlations and cross-twin–cross trait (CT–CT) correlations were estimated in saturated models in which all parameters  are freely estimated. We modeled variance components separately for males and females. For satisfaction with friendships, scores were highly skewed. To prevent bias , we tranc22), 2) we dropped the covariance explained by C (c21), and 3) we dropped C for the social variable (c11). If a C component could not be dropped for both sexes, we tested whether it could be dropped for males or females only. Additionally, we computed genetic and environmental correlations, reflecting the extent to which there is overlap in the latent genetic and environmental factors influencing the traits. Parameters were estimated using maximum-likelihood estimation in OpenMx [p value threshold of α = 0.005.Since the variance component approach does not yet allow for the inclusion of opposite-sex twins in estimating the variance components, DZ opposite-sex twins were excluded when estimating the variance components. We therefore could not test for qualitative sex differences. To test for quantitative sex differences , we constrained variance components to be equal across males and females and compared the fit of the constrained model to that of the less restrictive model. Next, we tested whether C significantly contributed to the (co)variance by dropping common environmental components in three steps : 1) we dropped C for well-being , less family conflict , more leisure time indoor games , more leisure time contact with friends , more leisure time sports club/scouting , a higher number of friends , and higher satisfaction with friendships (SWF) . Leisure time crafts, leisure time making music, leisure time computer, leisure time going out, leisure time TV, and importance of friendships were not associated with well-being.In the GEE analyses Table , higher β = − 0.15, p = 3.18 × 10–10, DZ: β = − 0.23, p =  < 2 × 10–16) and the FES difference score . This supports the hypothesis that there is either a causal relation between the two traits, or a large role for E. Given that the DZ difference scores are more strongly associated than the MZ difference scores, there is also a potential role for A.Differences scores for two social variables were significantly associated with well-being difference scores in both MZ and DZ twin pairs: the FAD difference score , and the SWF difference score . This suggests that A plays a large role in the association between well-being and these social variables.Difference scores for two variables were significantly associated with well-being difference scores in DZ twins, but not MZ twins: leisure time sport/scouting club , leisure time contact with friends , and number of friends . The lack of MZ and DZ difference score association indicates that shared environmental factors are likely the underlying source of the observed association between well-being and these social variables.Finally, three of the variables for which we observed a phenotypic association with well-being were not significant in the intra-pair difference score analyses for both MZ and DZ twins Table : leisureBased on the difference score analyses, four traits were followed up with bivariate genetic model fitting: FAD, FES, leisure time spend at sports/scouting club, and satisfaction with friendships. From the saturated models , we estimated the cross-twin and CT–CT correlations for each trait . For all traits, MZ correlations were higher than DZ correlations, indicating a role for A. Twin correlations for well-being and satisfaction with friendships in males indicated a potential role for D in the variance decomposition. However, as explained in the methods section, we only fit bivariate ACE models.All MZ CT–CT correlations were significantly different from zero. DZ correlations were either non-significant or smaller than MZ correlations for all traits except family conflict in males, suggesting that A has a substantial influence on the covariance between well-being and the social variables see Fig. . For famA = 35%, CI 29–41%) than females . In the bivariate model with well-being and FAD scores, all C components could be dropped for males, while the C component for FAD could not be dropped in females . The heritability (A) of FAD was 47% (CI 41–52%) for males and 25% (CI 11–39%) for females. The covariance with well-being was mainly explained by genetic factors . For FES scores, the best fitting model in males was an AE model , while the best fitting model for females was an ACE model . The phenotypic correlation between well-being and FES was explained mostly by genetic factors .The final model (co)variance decomposition results for all traits can be found in Fig. C = 33%, CI 23–43%). The heritability (A) was estimated at 60% (CI 56–64%) in males, and 33% (CI 22–44%) in females. Genetic factors contributed to 91% of the covariance between well-being and leisure time sports/scouting clubs in males (CI 60–126%) and to 89% of the covariance in females (CI 69–108%). The estimates of our variance components were unbounded, which led to confidence intervals outside the usual range of 0–1 for these last two traits. This, together with the twin and CT–CT correlations, indicates a potential role for D.For SWF, all C components could be dropped. The heritability (A) of SWF was estimated at 25% (CI 12–38%) in males and 35% (CI 29–41%) in females. Again, genetic factors explained the largest part of the covariation with well-being . Finally, for leisure time sport/scouting clubs, C could be dropped for males, but not females  and females  separately. All genetic correlations were significant, with negative genetic correlations between well-being and FAD  and FES , and positive genetic correlations between well-being and SWF (rAm = 0.54 rAf = 0.54) and leisure time sports/scouting club (rAm = 0.22 rAf = 0.30). Unique environmental correlations were significant for FAD  and FES  only.eTable 6 (Online Resources) contains the genetic and environmental correlations between well-being and the other traits for males , we performed bivariate genetic analyses. Common environmental influences did not contribute to associations with well-being, with genetic and unique environmental factors explaining the associations fully. For all traits, the largest part of the association was explained by genetic factors (between 73 and 91%). For females, a higher proportion of the association between well-being and the social traits was explained by genetic factors. For males, twin correlations indicated that D might contribute to variation in well-being and satisfaction with friendships. Additionally, CT–CT correlations for satisfaction with friendships and leisure time sport/scouting club also indicate a potential role for D. This is in line with the previous studies on well-being in adolescence, where a role for D was also indicated , 40.rEm = − 0.13, rEf = − 0.12 and rEm = − 0.12, rEf = − 0.12, respectively). While this does not falsify the claim that there might be a role for causality, this does indicate that a potential causal association will likely also be of small magnitude. Interestingly, we did not find a significant unique environmental correlation between well-being and satisfaction with friendships, suggesting that the association between those two traits is non-causal, at least in adolescence. While multiple studies identify an association between well-being and friendship quality/satisfaction [While we did not directly test for causality in this study, we can draw some inferences based on the genetic and environmental correlations. If there is a causal relation between two traits, it is expected that genetic and environmental factors influencing one trait also influence the other trait . If thesfaction , 42, thesfaction , 43. To These findings show that there is an important third factor in the association between well-being and several social variables in adolescence that is often unmeasured in psychological research: heritable influences. Phenotypic associations between well-being and different social variables are often found, but it appears that large parts of these associations are attributable to genetic factors. An interpretation of this genetic overlap is that the association between well-being and these variables is likely largely due to a genetic predisposition for appraising one’s life positively or negatively. For example, one might evaluate his or her well-being and friendship environment more positively in general because of their genetic predisposition for doing so. While this only pertains to the traits we now studied in more detail , an interesting question for future research would be to study if this genetic influence is also present for associations with other social variables . Additionally, genetic and environmental correlations indicated that causality might be at play in the associations between well-being and family conflict and family functioning, with similar genetic and environmental factors influencing both traits, potentially through a causal chain.An interesting follow-up for these findings is longitudinal studies, preferably using genetically sensitive designs. For example, if twin data are available, direction of causation models (if there are different modes of inheritance for the traits under study)  and geneIn conclusion, we examined associations between well-being and a set of socio-environmental variables and find that genetic factors play a large role in several of these associations, confirming the importance of taking genetic differences into account. Additionally, we find a potential role for causality in the association between family conflict/functioning and well-being, with overlap in the genetic and environmental factors that influence these traits. From a clinical perspective, the family environment thus forms an interesting target for improving adolescent well-being.Supplementary file1 (DOCX 79 KB)Below is the link to the electronic supplementary material.


Gregor Mendel’s discovery of the laws of segregation and independent assortment and his inference of the existence of non-mendelian interactions between loci remain at the heart of today’s explorations of the genetic architecture of quantitative traits. Mendel’s discovery of the laws of segregation and independent assortment and inference of the existence of non-Mendelian interactions between loci are at the heart of modern explorations of the genetic architecture of quantitative traits. Pisum sativum, Mendel chose 7 “characters” (polymorphic loci each affecting a different phenotype in today’s parlance) that “permit of a sharp and certain separation,” excluding those for which “the difference is of a ‘more or less’ nature, which is often difficult to define” [1) between the homozygous parental genotypes (to use modern terminology) was indistinguishable from one of the parents; i.e., one of the alleles was dominant and the other recessive . He excluded phenotypes that were intermediate between the 2 parents in the F1 hybrid.The field of genetics was born with the publication in 1866 of Gregor Mendel’s Experiments in Plant Hybridization [1 hybrids among themselves, he observed a 3:1 ratio of dominant to recessive phenotypes in the F2 generation. He inferred that the F1 hybrids have 2 alleles at each locus , which are present in equal proportions in the female and male gametes, and combine at random to produce F2 offspring in the ratio 1 AA:2 Aa:1 aa (the Law of Segregation). Mendel also performed crosses in which the hybrids differed for 2 or 3 of the 7 loci, analyses of which led him to conclude “the relation of each pair of different characters in hybrid union is independent of the other differences in the 2 original parental stocks” (the Law of Independent Assortment) [When Mendel crossed each of the 7 FMendel’s principles of inheritance were contrary to the common observation at the time that crosses between organisms with different phenotypes were often intermediate between the 2 parents, and that phenotypic variation in populations is continuous and not discrete . It was Quantitative traits include all aspects of an organism’s morphology, physiology, behavior, and fitness, as well as molecular phenotypes such as gene expression, and protein and metabolite abundances. Although the principles of mapping regions of DNA associated with particular traits, known as quantitative trait loci (QTLs), by linkage to mendelian loci have been understood since the beginning of the 20th century , these sWhat have we learned about the genetic basis of variation for quantitative traits from the thousands of studies mapping QTLs that have now been completed? First, quantitative traits are indeed highly polygenic, as suggested by Fisher . Second,These features of the genetic architecture of quantitative traits have implications for agriculture, human genetics, and evolution. For example, unless the relevant contexts are accounted for, attempts to improve traits of agricultural importance by transferring QTLs from breeds and varieties selected for performance under one set of conditions to breeds and varieties adapted to different conditions may not have the expected effects, and genomic prediction developed for one breed may not transfer to other breeds. Polygenic risk scores for human diseases that have been developed for one population may not be accurate in other populations unless specific interactions are included in the models. Identifying epistatic modifiers of rare human diseases could provide clues for therapies, and defining genotypes by their drug environment interactions will facilitate pharmacogenomic applications. Furthermore, context-dependent effects in natural populations may be in part responsible for the maintenance of quantitative genetic variation and adaptive evolution.Mendel’s experiments on plant hybridization have laid the foundation of modern quantitative genetics. Mendel was well aware of the significance of his discoveries . A few m


Sus scrofa chromosomes (SSC) 2, 7, 9 and 13 for VL or VW. GWAS based on imputed data identified 11 new quantitative trait loci (QTL) on SSC1, 2, 7, 8, 9, 11, 13, 16 and 17 for VL or VW. The most significant QTL for VL on SSC2 were refined to a 3.48–3.97 Mb region using linkage disequilibrium and linkage analysis (LDLA). In this refined region, FGF19 and CCND1, involved in the development of the reproductive tract, cell growth and vulvar cancer, could be new candidate genes affecting VL. Our results provided potential genetic markers for the breeding of vulvar traits in pigs and deepened the understanding of the genetic mechanism of vulvar traits.Vulvar size and angle are meaningful traits in pig production. Sows with abnormal vulva generally show reproductive disorders. In order to excavate candidate loci and genes associated with pig’s vulvar traits, 270 Suhuai pigs with vulvar phenotype were genotyped by a porcine single nucleotide polymorphisms (SNP) Chip. Then, Chip data were imputed using resequenced data of 30 Suhuai pigs as a reference panel. Next, we estimated the heritability and performed a genome-wide association study (GWAS) for vulvar traits. The heritabilities for the traits vulvar length (VL), vulvar width (VW) and vulvar angle (VA) in this pig population were 0.23, 0.32 and 0.22, respectively. GWAS based on Chip data identified nine significant SNPs on the  Pork accounts for a large proportion of the meat market in China. Hence, enterprises focus on breeding pigs with high fertility. Reproductive performance is one of the most important economic traits in the pig industry since it can directly affect the population size and production efficiency of a pig farm. In breeding, vulvar size and angle reflect the development of the genital tract of a sow. The vulvar size and angle of sows should not be too small, otherwise it is difficult to carry out artificial insemination and farrowing. In recent studies, Graves et al. found that sows with larger vulva at 95–115 days had a higher proportion of estrus within 180 or 200 days than sows with smaller vulva at the same period [https://www.animalgenome.org/cgi-bin/QTLdb/SS (accessed on 1 June 2022)). Corredor et al. recently detected multiple QTLs on SSC1 2, 5, 7, 8 and 10 significantly associated with vulvar size by using GWAS in Yorkshire and Landrace populations [BMP15 affected litter size of sows, but genes related to vulvar traits were not found in their study [Most reproductive traits have complex genetic background and the traditional breeding method makes it difficult to improve these traits. In recent decades, identifying SNPs related to reproductive traits and using these associated SNP markers for marker-assisted selection or genomic selection to improve reproductive traits have become effective methods of pig breeding. However, in terms of detecting causal loci, previous studies mostly used low-density molecular markers to map QTLs, and the identified candidate loci usually covered large genomic regions with less confidence . RecentlSuhuai pig is a synthetic pig breed, containing a 25% lineage of Chinese Huai pig and a 75% lineage of Large White pig . The vulAll experimental protocols and details were approved by the Nanjing Agricultural University Animal Care and Use Committee (Certification No.: SYXK (Su) 2017-0007).This study collected 270 Suhuai sows whose ages were about 160.0 ± 6.7 (Mean ± SD) days to measure the vulvar length, width and angle. All experimental individuals were raised in standard houses at the Huaiyin breeding farm . Vulvar length, width and angle were measured, respectively. Vulvar length is the distance from the top to the bottom of the vulva. Vulvar width is the distance from the leftmost to the rightmost end of the widest part of the vulva. The measurement rules of vulvar length and width were consistent with that of Mills et al. . Vulvar Ear tissues from 270 individuals were used to extract DNA by the phenol/chloroform method. Quantification and quality testing of genomic DNA was completed by using NanoDrop 2000 . DNA with a concentration of greater than 50 ng/μL and a value of 260/280 ranging from 1.7 to 2.1 were furthered used for SNP genotyping. For these 270 samples, 211 samples and 59 samples were genotyped using a GeneSeek GGP Porcine 80K Chip and COMPASS Porcine 50K Chip, respectively. The quality control of genotyped data was performed by using PLINK v1.09 software  and the Meanwhile, 30 Suhuai pigs which represented the lineage of male and female pigs in the core group of Suhuai pigs were resequenced. SNP calling and filtering were executed by GATK 4.0 , followihttps://odamid-apse1.oda.sas.com/SASStudio (accessed on 13 April 2022)). Significant factors were used in subsequent models. For vulvar size, birth season and measurement batch were used. For vulvar angle, only measure batch was used. Genomic data is more accurate in predicting breeding values than pedigree data [Descriptive analyses of phenotypes including VL, VW and VA were conducted in R 3.6.1 software. The effect of environmental factors on phenotype was fitted by the general linear model in SAS 9.4 (ree data . In thisree data  based onThe model (Model 1) of heritability estimation contains the following effects:software .p-value < 1/N were considered as suggestive significant loci [p-value distribution of SNPs and the latter was the comparison between the expected p-value and the realistic p-value. LD decay distance was calculated by the PopLDdecay tool [2) was 0.2, the LD decay distance of 270 Suhuai pigs was about 300 Kb  was performed by PLINK v1.09  to test g et al. . In the g et al. . This meg et al. . A largeg et al.  and theng et al. . Thus, tant loci . The CMpant loci  was usedcay tool  using 46t 300 Kb . Thus, Q10p of the most significant locus decreased by 2 [LDLA was conducted to refine QTLs identified by GWAS. Similar to the process of GWAS, LDLA calculated the correlation between haplotypes and phenotypes. Haplotypes were constructed through beagle 3.0 . The modsed by 2 . The codsed by 2  and was The formula variance . In thishttp://asia.ensembl.org/index.html (accessed on 3 May 2022)) and their biological functions were retrieved in Pubmed (https://pubmed.ncbi.nlm.nih.gov (accessed on 3 May 2022)).The positions of SNPs in Chip were determined according to SSC10.2 reference genome and were converted according to SSC11.1 reference genome. Genes located in intervals refined by LDLA were considered as candidate genes. Candidate genes were further searched for their function details in ensemble database  of VL, VW and VA for Chip data were 1.076, 1.077 and 1.076, respectively, and for imputed data were 1.102, 1.072 and 1.074, respectively.Firstly, a total of 46622 SNPs were used for preliminary GWAS analysis . Among tFGF3, FGF4, FGF19), LTO1 maturation factor of ABCE1 (LTO1) and cyclin D1 (CCND1) were located at this interval. After functional annotation, FGF19 and CCND1, involved in the development of the reproductive tract, cell growth and vulvar cancer, were identified as the possible candidate genes of vulvar traits.In GWAS analysis, QTL located in 3.25–4.25 Mb on SSC2 had a considerable impact on VL. To refine this QTL interval, imputed data were used to perform the LDLA analysis of this QTL. The region of 3.48–3.97 Mb on SSC2 was identified as a candidate interval by LDLA . The comn = 211) or 50K Chip (n = 59) and 30 Suhuai pigs were resequenced using the whole genome sequencing. Two-step imputation was used to increase the marker density of data. Average imputed accuracy from 50K Chip to 80K Chip and from 80K Chip to resequenced data were 99.1% and 93.7%, respectively. Our results showed that the imputed accuracy could be related to the size of the reference population and the degree of difference in marker density between the reference population and the target population. Low density SNP Chips were directly imputed into the high-density data of the whole genome, which would affect the quality of imputation. Xu et al. imputed the 50K Chip of 246 boars into the resequenced data and obtained a genotypical accurate rate of 84.8% [This study measured the vulvar phenotype of 270 Suhuai pigs in total. Through descriptive analysis, the three vulvar phenotypes were obviously separated in this population, especially regarding VL and VW, which showed that this population was helpful for excavating candidate genes affecting vulvar traits. Then, these pigs were scanned by using 80K Chip (of 84.8% . Yan et of 84.8% . TherefoIn previous studies for heritability estimation, Knauer et al. recorded the vulvar width of 1225 Landrace×large white crossbred sows with an average age of 162 days, and the estimated heritability of vulvar width was 0.58 . CorredoBased on Chip data and imputed data, several QTLs that might affect vulvar traits were mapped by GWAS. For QTLs detection, seven significant SNPs for VL or VW on SSC2, and 13 obtained by Chip data were still significant using imputed data in our study. In addition, compared to Chip data, the significant loci obtained by imputed data were more significant. The results of these two comparisons indicated that the imputed method was helpful for further excavating candidate genes. In other studies, Corredor et al. identified multiple QTLs for VL on SSC1, 2, 5, 7 and 10 and for VW on SSC1, 2 and 8 [FGF19 belonged to the FGF family and this family was involved in the development of the female reproductive tract [FGF19 promoted glycogen synthesis and stimulated protein synthesis [CCND1 is a kind of cyclin coding gene in mammals and plays an important role in regulating cell division cycle and cell growth [CCND1 was detected to be overexpressed in vulvar cancer tissues of most patients [FGF19 and CCND1 on SSC2 might be two novel functional genes affecting the vulvar length. Follow-up studies are needed to excavate the functional loci of the two genes affecting vulva traits and reveal the molecular mechanism of regulating vulvar development.In addition to GWAS, LDLA was also an effective method to map and refine QTLs ,30. Our ve tract . Furtherynthesis . CCND1 il growth . In a stpatients . Taken tFGF19 and CCND1 might be new genes related to the VL of pigs. These findings will provide reference for understanding the molecular genetic basis of pig’s vulva-related traits.Vulvar traits including VL, VW and VA showed moderate heritability in Suhuai pigs. A total of 11 new QTLs for VL or VW on SSC1, 2, 7, 8, 9, 11, 13, 16 and 17 were identified by GWAS. Combining GWAS and LDLA, a 3.48–3.97 Mb region on SSC2 was identified as an important QTL with a larger effect on VL, and significant SNPs in this QTL could be potential molecular markers for pig breeding. In addition,


Forensic DNA Phenotyping (FDP) can reveal the appearance of an unknown individual by predicting the ancestry, phenotype , and age from DNA obtained at the crime scene. The HIrisPlex system has been developed to simultaneously predict eye and hair color. However, the prediction accuracy of the system needs to be assessed for the tested population before implementing FDP in casework. In this study, we evaluated the performance of the HIrisPlex system on 149 individuals from the Turkish population. We applied the single-based extension (SNaPshot chemistry) method and used the HIrisPlex online tool to test the prediction of the eye and hair colors. The accuracy of the HIrisPlex system was assessed through the calculation of the area under the receiver characteristic operating curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The results showed that the proposed method successfully predicted the eye and hair color, especially for blue (100%) and brown (95.60%) eye and black (95.23) and brown (98.94) hair colors. As observed in previous studies, the system failed to predict intermediate eye color, representing 25% in our cohort. The majority of incorrect predictions were observed for blond hair color (40.7%). Previous HIrisPlex studies have also noted difficulties with these phenotypes. Our study shows that the HIrisPlex system can be applied to forensic casework in Turkey with careful interpretation of the data, particularly intermediate eye color and blond hair color. Genome-wide association studies (GWASs) have been a powerful tool for unraveling the molecular genetic basis underlying natural human phenotypic variations. Through GWAS, many single nucleotide polymorphisms (SNPs) that are involved in human phenotypic variations  have been identified ,2,3. ForThe first FDP study started with the identification of the MC1R (melanocortin 1 receptor) gene associated with the red hair and freckles’ variation in normal populations . More thIn the last years, the HIrisPlex system, which includes a single multiplex genotyping assay for 24 SNPs, was developed by the same research group by extending the eye color panel with additional 18 SNPs that are associated with hair color variations . The panBuccal swabs were taken from 149 unrelated volunteers  living in Istanbul, Turkey. All individuals were randomly selected, except for four red-haired individuals who were invited because red hair is not widespread in Turkey. The samples were also selected according to their birthplace and the parents’ birthplace to reflect seven geographical regions  of Turkey. All volunteers were asked to complete a questionnaire that included basic information such as gender, ancestry, age , and data concerning eye and hair pigmentation phenotypes. The volunteers’ statements on their hair and eye colors were recorded on consent forms. The photos were taken in portrait mode with equal light intensity using a Nikon D5100, 18–55 mm lens at an equal distance. Two independent researchers assigned each volunteer’s eye and hair color into categories , and brown; for hair color: red, blond, brown, and black), according to the phenotyping regimes applied by Walsh et al. ,15,16. TEye colors were classified into three categories: brown (61.07%), intermediate (25.50%), and blue (13.42%). Hair colors were classified into four main categories: blond (19.45%), brown (63.75%), black (14.7%), and red (2.68%).® fluorometer using the QubitTM dsDNA HS Assay Kit , according to the manufacturer’s protocols. Genomic DNA was purified from buccal swabs using the QIAamp DNA Mini Kit . The amount of extracted DNA was measured with the Qubit® reaction mix (Thermo Fischer Scientific), 1.61 μL of SBE primer mix (0.1 μM), 0.39 μL nuclease-free water, and 2 μL of purified DNA under the following conditions: 30 cycles of 96 °C for 10 s, 50 °C for 5 s, and 60 °C for 30 s. Excess nucleotides were removed by the addition of 1 μL SAP to the total volume of extension products, and incubation at 37 °C for 80 min, and then by enzyme inactivation at 85 °C for 15 min. Capillary electrophoresis was performed using an ABI PRISM 3130 Genetic Analyzer (Thermo Fischer Scientific). The matrix standard DS-02 and POP-4 polymers were used on the AB PRISM 3130 Genetic Analyzer with filter set E5 to process the data. Genotypes were generated with GeneMapper v4.0 (Thermo Fischer Scientific) or Peak Scanner Software v2.0 (Thermo Fischer Scientific).We used the HIrisPlex panel, a 24-plex assay (23 SNPs and 1 InDel) that has been developed over the years for predicting eye and hair color . HIrisPlhttps://hirisplex.erasmusmc.nl/, accessed on 14 November 2021). The model calculates the prediction probabilities for the eye  and the hair  colors using the multinomial logistic regression (MLR) model. For eye color, the threshold was set as 0.7 as suggested by Walsh et al. [Phenotype predictions were performed using the online HIrisPlex tool  and the receiver operating characteristic (ROC) curves using a script written in Python 3. Moreover, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model were calculated according to Liu et al. . The senWe also compared the allele frequencies of the HIrisPlex SNPs with the European population data from 1000 Genomes Phase 3  to explore the ancestry differences between populations. SNP frequencies of the Turkish population were calculated by gene counting.In this study, we evaluated the performance of the HIrisPlex system for the Turkish population. Before testing the population samples, we completed an internal validation study according to the validation guidelines for forensic DNA analysis methods of the Scientific Working Group on DNA Analysis Methods (SWGDAM) . In the To determine the effect of ancestry on the phenotype prediction, we compared the Turkish population allele frequencies with the European population from the 1000 Genomes Project Phase 3 data. The allele frequencies of our population were very similar to that of the overall European 1000 Genomes population data except for SNP rs683, rs16891982, and rs12913832 . rs683 ip > 0.7) for eye color predictions. After applying the threshold value, we observed the prediction success for blue eye of 100% (n = 20) and brown eye of 95.60% (n = 91) in the Turkish population samples  and intermediate 100%) eye colors while the specificity was lower for brown (65.51%) eye color. This can be explained by the high number of intermediate-eye-colored individuals who were classified as mostly brown . We also0% eye coLiu et al. (2009), who developed the prediction model, tested the IrisPlex SNPs on 6168 Dutch European populations. The accuracy of the eye color prediction was over 90% for blue and brown eye colors . In our The success of the blue and brown eye color predictions of the HIrisPlex (or IrisPlex) system has been proven by different researchers who tested different population samples. Similarly, a high percentage of false predictions for intermediate eye color have been reported ,26,27,28p-value from the four phenotype classes . Then, within the highest categories, the dark or light p-values for color shade were considered for the final phenotype prediction [n = 21) for black, 96.90% (n = 97) for brown, 59.25% (n = 27) for blond, and 75% (n = 4) for red, respectively , brown (0.89), blond (0.90), and lower for red (0.55) . The obtp-value as described by Walsh et al. [p-values were detected in the brown class for 11 individuals whose actual hair color was blond or dark blond. Therefore, these individuals were incorrectly predicted to have brown hair  among the other phenotype categories .The lowest sensitivity value was obtained for blond hair as a result of the highest proportion of incorrect predictions . The bloh et al. . In our own hair . For exaThe specificity was highest for the red, black, and blond hair colors . The specificity of the brown hair color was lower (76.92) due to incorrectly predicted individuals (mostly blonds) as brown. Hence, in an investigation, if the unknown sample is predicted as brown, the true hair color is highly possible to be brown, but also possible to be blond and maybe black hair color. Therefore, the analyst should consider that the suspect could have hair color varying from dark blond to black.The HIrisPlex panel includes 22 SNPs for hair color prediction . The HIrp-value of the light color shade provides additional information for blond hair color predictions [The HIrisPlex model yields additional hair color shade prediction values (light and dark) to aid in the better classification of each hair color. The dictions ,29.The EVCs  are the most visible variations between populations. Individuals from different geographical regions may have different SNP variations that might affect their EVCs. In this study, we observed allele frequency differences in three SNPs  between the Turkish and European populations. These allele frequency differences again show how important it is to test the HIrisPlex panel in non-European populations. As discussed above, the prediction performance of the eye and hair colors were reasonable for the blue and brown eye colors, and black and brown hair colors. However, non-European populations have more variation in the intermediate phenotypes compared to Europeans, which resulted in false predictions for those phenotypes. Therefore, the interpretation and reporting should be made more carefully for these phenotypes and potential errors should be addressed. Improvement in the prediction of challenging phenotypes needs further studies for a better understanding of the genetic structure and additional new markers.The determination of intermediate eye and blond/brown hair colors may be subjective. To eliminate subjectivity, it is necessary to standardize the collection and reporting of EVCs. To increase consistency, the standardization of nomenclature is also required . MoreoveIn this study, we assessed the performance of the HIrisPlex system (SNaPshot method and MLR model) for hair and eye color prediction in the Turkish population. We determined the prediction performance by calculating the sensitivity, specificity, AUC, PPV, and NPV values of the HIrisPlex panel. We obtained high prediction accuracies (over > 95%) for blue and brown eye colors and black and brown hair colors. The prediction errors were observed in the more complex phenotypes such as intermediate eye color and blond hair color. Despite the challenges with these phenotypes, the HIrisPlex system can be reliably applied to the Turkish population when interpreted with caution.


The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage  between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population ( Forest trees are slow growing, long-lived species that require multiple years to reach reproductive maturity . These aPinus taeda L., the breeding cycle begins with a controlled cross between two individuals in a breeding orchard. After 18 months of cone maturation, seeds are extracted and used for progeny test establishment. These progeny tests are measured 4–5 years after planting. Traditionally, individual tree breeding values are predicted with pedigree-based best linear unbiased prediction (ABLUP) (In the conifer  (ABLUP)  and sele (ABLUP) . If geno (ABLUP) .Many proof-of-concept studies using real datasets have been conducted to examine factors related to the success of GS in forest trees . In the Most of the proof-of-concept studies of GS in tree species utilized random cross-validation schemes that included familial relatives of the prediction set in each training set. Since the loblolly pine breeding program managed by the Cooperative Tree Improvement Program at North Carolina State University utilizes a full-sib mating design with many crosses produced each year , a realiThe first objective of this study was to measure the ability of a clonal training population to predict genomic estimated breeding values (GEBV) of individual trees in a separate population. The second objective was to study the effect of genetic similarity between the training and validation population on prediction accuracy. We genotyped a number of trees from wide-ranging progeny tests with varying degrees of relationship with the clonal training population. Genetic architectures for stem height and straightness  were studied through individual trait genome-wide association studies (GWAS), and significant markers were tested as fixed-effect covariates in GBLUP models. Genome-wide analysis of linkage disequilibrium (LD) was conducted to contextualize the modeling results and develop recommendations for the practical implementation of GS in conifer breeding programs.Cronartium quercuum (Berk.) Miyabe ex Shirai f.sp. fusiforme at the USDA Forest Service Resistance Screening Center in Asheville, NC using an artificial inoculation. From the original set of 76 full-sib families, 25 were removed due to high susceptibility to disease. This resulted in the removal of three parents from the original disconnected diallel mating design.The population used for model training, Atlantic Coastal Elite (ACE), is a clonally replicated collection of full-sib families established across eight environments in the early 2000s . A totalAn average of 46 full-sib progeny from each of the remaining 51 families was vegetatively propagated. From each progeny, eight rooted cuttings (ramets) were produced. These ramets were established in eight environments across the southeast United States . Each enTests were measured 6 years after establishment. Height was measured in units of feet and was converted to meters. Stem form was recorded as an ordinal variable with values from 1 to 6, with 1 being the straightest. At the time of measurement, a total of 2499 clonal genotypes were present across the eight tests. These genotypes were members of 51 full-sib families and 7 open-pollinated families.In order to test the ability of GS models trained within a clonal population to predict GEBVs of individual trees in a separate population, a group of trees with both genotypic and phenotypic records were required. These trees were sampled from a set of 18 progeny tests from the fourth Cycle Coastal breeding population of The North Carolina State University Cooperative Tree Improvement Program . Each loThe average number of shared parents between pairs of fourth cycle progeny tests was 100.5, representing 72% of all parents tested within any given site. The connectivity at the level of full-sib families was 59.2 for pairs of fourth cycle tests, representing 51% of all full-sib families at any given site. The fourth cycle progeny tests were connected to the ACE clonal tests through an average of 18.6 shared parents . ConnectAll fourth cycle tests were measured 4 or 5 years after establishment for the same traits measured in the ACE clonal tests. A set of 451 individual trees was sampled from the fourth cycle tests for testing the ability of GS models to predict individual tree GEBV. In the set of individual samples, 52 were members of a full-sib family that was also represented in the ACE training population, 213 trees were members of seven half-sib families that shared one parent in common with the ACE training population, and 186 had no direct parental relationships with the training population. Each half-sib family contained two or more nested full-sib families.dplyr and ggplot2 packages were used extensively throughout this work  was used for two purposes: GWAS and GBLUP. For GBLUP, all 2063 ACE clonal genotypes plus 451 individual trees sampled from fourth-Cycle progeny tests were used. The marker matrix used in GBLUP will be referred to as G was calculated from M1 using the R package AGHMatrix  matrix for use in GWAS.The genomic relationship matrix popkin  for the ACE clonal population for GWAS. The model was:y was a response vector for 14,857 vegetative ramets of 2,494 clones. y to the population mean, eight test sites, and 2494 clones. The vector of fixed effects Z is a  design matrix for random effects, mapping the records in y to 360 column-within-test effects, and 518 row-within-test effects. The random effects vector where R.The assumptions of the model were that t, t, and t.where GWAS function of the R package rrBLUP was used as a mixed-model platform for association analysis. The following linear mixed model was fit for each marker:The y represents a vector  of pseudo-phenotypes [the BLUE of genotyped ACE clones estimated in (Eq. 1)]. y to the loadings for each clone on the first three principal components of the kinship matrix, represented in this model by matrix y to the polygenic effects in g. Finally, the fixed effect of each marker was obtained using a design matrix eigen  for a single full-sib family. The design matrix X is a vector of 0’s and 1’s, representing homozygous and heterozygous genotypes at the significant marker, while b represents the regression coefficient (the average effect). Here, with Univariate animal models were used to predict breeding values for all genotypes in the ACE and fourth-Cycle populations. The animal model took the form:y being a vector  of phenotypic records (height or straightness) for 14,857 trees (vegetative ramets) in ACE clonal tests and 13,026 individual trees in fourth-Cycle. Matrix X is a  design matrix mapping the fixed effects in y. The fixed effects vector 1Z had dimensions . The random experimental design effects vector, 2Z, had dimensions . The vector of random genetic effects, R, was block-diagonal with a separate residual variance for each location.with G, had two sub-matrices rep,” “cr,” “rr,” “ct,” and “rt,” respectively. The variance–covariance matrix for the random genetic effects, SCA,” “u,” and “u.e,” respectively. In this formulation (CORUV structure in ASReml software syntax), a single variance parameter is estimated for genotype-within-environment effects, and a single covariance is estimated for among-environment genetic covariance. These parameters are interpreted as A had dimensions of .The assumptions of the model were that th individual within the tth environment. Reliabilities were computed using the prediction error variance (PEV) of each BLUP: th individual.Marginal estimated breeding values (EBV) were obtained by averaging the genotype-within-environment effects for each genotype over 26 tests: To test the ability of genome-wide markers to predict additive genetic merit of trees, a GBLUP model was applied to the same dataset that was used for the estimation of BLUE of the ACE clonal population, excluding non-genotyped clones. In addition, 451 rows were appended to the bottom of the dataset representing the genotyped fourth-Cycle trees, but their phenotypic data were not included in the model. The mixed linear model for GBLUP can be written as before:y represents the response vector for 12223 ramets  and the fourth cycle population 451 individual trees. No phenotypic data were included for the individual trees. The design matrix for the fixed effects ε.here, G representing the realized relationship matrix calculated from SNP markers using Method 1 from G instead of A.The variance–covariance matrix for the additive genetic effects was A significant GWAS association on linkage Group 5 for tree height was used as a fixed-effect covariate in GBLUP. The average effect of the allele substitution :t is the number of fourth-Cycle progeny tests. Since the objective was prediction of individual tree breeding values, the residual term where Heritability of clone means was estimated in the same manner, except that the residual term was weighted by the harmonic mean number of vegetative ramets per clone  within the ACE clonal population. In each of 10 replications of Random-CV, the phenotypic data for a random set of 20% of the ACE clonal genotypes was held out of the model, and GEBV for these clones were predicted using the other 80% of the population. Since the sampling was random, the training population for each clone was a mixture of full-sib, half-sib, and distant relatives.The second cross-validation scenario was full-sib cross validation (Fullsib-CV). In this scenario, each of 51 full-sib families in the ACE population was held out of the model in turn, and predicted using the other 50 families. This scenario represents a common breeding situation in which a new full-sib family is produced that is not represented in the training population. In this scenario, the training population for each clone lacked full-sib relatives, but contained a mixture of half-sib and more distantly related relatives.In each replication of cross-validation, variance parameters were fixed using estimates from the full model . Since the breeding program conducts forward selection using individual tree BLUP (EBV), prediction ability in each fold was measured as Since the fourth-Cycle progeny tests were not included in the training model for GBLUP, cross-validation could not be replicated for the 451 genotyped trees. The impact of relatedness on prediction ability was assessed using the full GBLUP model trained with all ACE clones, and prediction ability was measured as the correlation between their GEBV from GBLUP and their EBV from ABLUP.M2 using the R package pegas  relative to Random-CV . The aveReliabilities of individual tree GEBVs were strongly influenced by the degree of relationship with the training population . For treGenomic prediction ability for individual trees in fourth-Cycle progeny tests was far lower than for clones within ACE. For tree height, genomic prediction abilities dropped from 0.58 for clones to 0.29 for individual fourth-Cycle trees . A reducThe average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form . For the2 close to 0.1  contains the genomic marker data for 29135 SNP markers on the Pita50K Affymetrix Array for all 2514 genotyped samples referenced in the manuscript. File M2.csv (https://doi.org/10.6084/m9.figshare.15023316) contains the phased genomic marker data, in “A|G” format, for 8437 SNP markers on the Pita50K Affymetrix Array for 2063 clones within the ACE training population. File phenotypes_ABLUP.csv (https://doi.org/10.6084/m9.figshare.15023343) contains the phenotypic dataset combining the 8 locations from the ACE1 clonal trials with the 18 fourth-Cycle progeny test locations. File pedigree.csv (https://doi.org/10.6084/m9.figshare.15023358) contains the pedigree file used in ABLUP. File phenotypes_GBLUP.csv (https://doi.org/10.6084/m9.figshare.15023508) contains the phenotypic data for genotyped ACE1 clones, as well as an additional 451 rows (lacking phenotypic data) for the individual genotyped fourth-Cycle trees. File Height_BLUE.csv (https://doi.org/10.6084/m9.figshare.15025266) contains the fixed effect estimates for height for all genotyped clones within ACE1. File “SupplementaryTable4.xlsx” (https://doi.org/10.6084/m9.figshare.15023334) contains the genetic map for all markers in M2.csv. File “GBLUP_pedigree.csv” (https://doi.org/10.6084/m9.figshare.15024540) contains the genotype identifiers and row order for the G matrix, to be used by GBLUP.as. File G1.grm.csv (https://doi.org/10.6084/m9.figshare.15024387) is a sparse-formatted realized relationship matrix computed from 29135 markers using Van Raden Method 1, to be used by GBLUP.as. File GBLUP.as (https://doi.org/10.6084/m9.figshare.15024876) is an ASReml job file for running the full GBLUP model as well as Fullsib-CV for height and stem form. File ABLUP.as (https://doi.org/10.6084/m9.figshare.15024705) is an ASReml job file for running pedigree BLUP for the combined ACE + fourth Cycle dataset. All pedigree identifiers have been coded with random alphanumeric strings.File M1.csv (jkab405_Supplementary_DataClick here for additional data file.


Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R Polygenic risk scores are used to improve risk prediction for common diseases but typically have reduced accuracy for individuals of non-European ancestry. Here, the authors present an approach that improves polygenic risk score performance in ancestrally diverse populations. Polygenic risk score (PRS) based on GWAS, typically calculated as a weighted sum of trait-associated allele counts across numerous loci in the genome, is an effective tool to quantify the aggregated genetic propensity for a trait or disease8. With rapid advances in GWAS sample size and statistical methodology for modeling summary-level data, PRS has shown substantially improved prediction accuracy and great potential in disease risk screening and precision medicine11. However, since the vast majority of GWAS participants are of European descent, current PRS models are more effective in Europeans but are known to have substantially reduced accuracy in other populations, which severely limits their clinical utility16. There is an urgent need to improve the effectiveness of PRS in diverse human populations and provide equitable access to genomic advances in precision medicine20.Genome-wide association studies (GWAS) have identified tens of thousands of genotype-phenotype associations for human complex traits23. Second, several studies combine multiple PRS trained in various populations using linear regression to optimize the predictive performance in the target (non-European) population24. The third type of approach parametrizes the degree to which genetic effects are correlated across populations, and integrates GWAS summary statistics from multiple populations in a multivariate model to improve effect size estimation and prediction accuracy in each respective population27. These models have achieved moderately improved predictive performance compared to conventional single-population approaches, but several critical limitations and challenges remain. First, previous studies used epigenetic regulatory annotations to prioritize variants for PRS23. While these annotations improved PRS portability for some traits, they are not designed to quantify the correlated genetic effects between populations28, and there is no guarantee that the same set of annotations will improve PRS performance for all complex traits. Additionally, existing statistical frameworks that leverage functional annotation data to improve PRS33 do not apply to multi-ancestry predictive modeling. Finally, in order to combine multiple population-specific PRS, the current practice requires additional data from the target (non-European) population. This includes individual-level genotype and phenotype samples that are independent of the GWAS used to train single-population PRS. In practice, this type of data can be nearly impossible to obtain34. In order to have broad applications, PRS models need to use the increasingly accessible GWAS summary statistics from global populations37 as input.There have been three types of approaches to improve cross-ancestry genetic prediction in the literature. First, prioritizing causal variants using functional genomic annotations can improve the portability of PRS based on European GWAS38 to directly quantify correlated (portable) genetic effects between multiple ancestral populations. Second, we introduce a Bayesian method to incorporate functional annotation data into multi-population PRS modeling, where annotation-dependent statistical shrinkage amplifies the effects of annotated variants . Finally, we resolve a long-standing challenge in the field and introduce a method to combine multiple PRS trained in various populations using GWAS summary data alone as input. We demonstrate the superior performance of X-Wing PRS through extensive benchmarking using numerous GWAS datasets, including UK Biobank (UKB)39, Biobank Japan (BBJ)40, and Population Architecture using Genomics and Epidemiology Consortium (PAGE) study41.In this work, we introduce a cross-population weighting (X-Wing) framework for genetic prediction. There are three main innovations in our approach. First, we introduce an annotation framework based on cross-population local genetic correlation. This annotation extends our previous work38 for identifying genomic regions with correlated effects on two complex traits. In this paper, we first extend this approach to identify correlated genetic effects on the same trait between two populations. Once identified, these genomic regions explain the shared genetic basis of the phenotype between populations and could be an informative annotation for prioritizing single-nucleotide polymorphisms (SNPs) in PRS models. Next, to quantitatively incorporate this annotation in multi-population PRS modeling, we introduce a Bayesian framework in which annotation-dependent shrinkage parameters allow variable degrees of statistical shrinkage between annotated and non-annotated SNPs. Coupled with other shrinkage parameters that do not depend on functional annotations, this framework amplifies SNP predictors that show correlated effects between populations while ensuring robustness to diverse types of genetic architecture45. Although we only explore its performance using the annotation derived from local genetic correlation in this paper, we note that this is a general framework that allows an arbitrary collection of annotation variables as input and also accounts for population-specific linkage disequilibrium (LD) and allele frequencies. Finally, we introduce an innovative strategy to linearly combine multiple PRS trained in different populations using summary association data alone. We employ a summary statistics-based repeated learning approach motivated from our recent work8 and its extension33 to estimate the regression weights for combining multiple PRS. The entire X-Wing procedure only requires GWAS summary data and LD references as input, which is a major advance compared to existing approaches. We present the statistical details and technical discussions in Methods and Supplementary Methods.The X-Wing workflow is illustrated in Fig. 46, we simulated chromosome 22 genotypes of 50,000 individuals, and simulated quantitative traits in two populations under an infinitesimal model with varying heritability levels (Methods). When the traits in two populations are independent, X-Wing showed well-controlled type-I error rates 48. We also investigated binary phenotypes using a liability threshold model. We obtained consistent results in these analyses, with our method showing well-controlled type-I error 39 and BBJ 40. In total, we identified 4160 regions with significant cross-population local genetic correlations across 31 traits . As a comparison, we also applied PESCA to these data, and identified 1,968 risk regions shared by two populations  which is consistent with previous reports50. But even for basophil count, we observed a substantial genetic correlation in regions identified by our approach (rg = 0.83). To guard against statistical artifacts, we performed falsification tests by simulating a trait that is uncorrelated between populations (Methods). We did not identify significant global or local correlations for this simulated trait  to 1.73% (height) of the genome, but explain 13.22% (diastolic blood pressure) to 60.17% (mean corpuscular volume) of the total genetic covariance between Europeans and East Asians Fig. , showingait Fig. .Fig. 3X-N = 95,454~100,184)51 and East Asian GWAS from the Asian Genetic Epidemiology Network 52 as the replication datasets  in independent data. We used European GWAS from the Global Lipids Genetics Consortium . In this analysis, our approach jointly models GWAS in two populations and outputs separate SNP weights for Europeans and East Asians (Methods). Here, we used annotation-informed PRS based on posterior SNP effects estimated for Europeans, and report its performance in the East Asian target sample . PRS performance is quantified using partial R2 adjusting for covariates (Methods). Our annotation-informed PRS showed a 4.6% (Pwilcoxon = 7.0e-6) and 35.2% (Pwilcoxon = 1.0e-7) median relative improvement in R2 compared to PRS-CSx14 and XPASS20  using our approach. PRS using only SNPs outside annotated regions did not show any improvement  relative gain in R2, suggesting that fine-mapping in European population alone is a sub-optimal approach compared to multi-population joint modeling , 46.1% (Pwilcoxon = 1.9e-9), and 24.7% (Pwilcoxon = 0.02) compared to PRS-CSx, XPASS, and PolyPred+ in East Asian target samples, respectively  and 1.9% (Pwilcoxon = 0.01) median relative increase in R2 compared to PRS-CSx in admixed Americans and Africans, respectively  in East Asians, 14.1–74.2% (Pwilcoxon = 4.8e-4 to 2.4e-4) in admixed Americans, and 30.2–119.1% (Pwilcoxon = 0.01–2.4e-4) in Africans , 9.6% (Pwilcoxon = 0.02), and 20.2% (Pwilcoxon = 2.4e-4) for traits in East Asians, Africans, and admixed Americans, respectively , instead of regressions trained on reserved samples, to linearly combine multiple PRS. This analytic strategy showed almost identical results compared to the gold-standard regression approach in both East Asian, admixed American, and African target samples  Fig. . Notablyans Fig. , 20, 21.In this paper, we introduced X-Wing, a sophisticated statistical framework for improving PRS performance in ancestrally diverse populations. X-Wing quantifies cross-population local genetic correlation, and incorporates it as an annotation into a Bayesian framework which amplifies correlated SNP effects between populations through annotation-dependent statistical shrinkage. It also combines multiple population-specific PRS to further improve prediction accuracy while using GWAS summary data alone as input. Applied to numerous GWAS traits, we demonstrated that local genetic correlations help pinpoint portable genetic effects and the annotation-informed PRS shows consistently and substantially improved performance across populations.57 (Supplementary Methods). It may also be applied to improve PRS portability across other non-ancestry-related demographic groups58. Finally, we introduced a strategy to combine multiple population-specific PRS into one improved score using summary statistics alone. This is innovative since fitting a regression model in an independent sample has long been considered the standard (and only) approach for combining multiple scores. This represents a significant advance in the field since obtaining additional individual-level samples that are independent from input GWAS can be a major challenge in practice. This is also generalizable since the same technique could be used to improve any PRS by creating an “omnibus” score over a number of methods, and the application is not limited to trans-ancestry risk prediction.Our study presents several methodological innovations that will likely be generalizable and impactful. First, we introduced the concept of cross-population local genetic correlation and developed a scan statistic method to map correlated regions. Complementary to global genetic correlation, local genetic correlation refines the resolution in identifying shared genetic components between populations and provides critical insights into the genetic architecture of complex traits in diverse human populations. Second, we developed a new Bayesian framework that allows the integrative analysis of functional annotation data in multi-population PRS modeling. In this work, we showcased its effectiveness in cross-population risk prediction using an annotation derived from local genetic correlations. But we note that it is a general framework that can incorporate arbitrary sets of annotation data, such as the epigenetic annotations used in the PRS literature, in silico variant annotations based on machine learning exercises, or LD and allele frequencies which have been shown to improve heritability estimationGATA2 at this locus encodes a zinc-finger transcription factor which plays an essential role in proliferation, differentiation, and survival of hematopoietic cells59. In particular, expression of GATA2, coupled with CCAAT enhancer-binding protein α (C/EBPα) and transcription factor STAT5, directs the differentiation of granulocyte/monocyte progenitors (GMPs) into basophils61. Another correlated region for basophil count is a locus spanning 51 KB on chromosome 3 , lies 13 KB away from the identified region. Binding of the receptor to its ligand IL-5 is required for the biological activity of IL-5. Notably, IL-5 is a human basophilopoietin that promotes the formation and differentiation of human basophils63. Many other traits have interesting findings too. For example, a region spanning 48 KB on chromosome 1 is associated with C-reactive protein in two populations  than expected between two populations. Fifth, only categorical annotations were used for PRS construction in our analysis. It may be of interest to directly estimate local genetic correlation first, and then incorporate the correlation values as a quantitative annotation to improve PRS.Our study also has some limitations. First, although our method does not require any individual-level sample with both genotype and phenotype information, it remains crucial to have LD reference panels that match the input GWAS. We observed an improvement in PRS performance when applying our method to highly diverse samples such as the PAGE study, but it remains unclear how to best select LD references for multi-ancestry GWAS and admixed populations23 should still be a strategy for consideration.Finally, the overall superior performance of X-Wing can be attributed to the incorporation of cross-population local genetic correlation and summary statistics-based PRS combination. Although we anticipate improved prediction accuracy after incorporating the local genetic correlation annotation, imprecise estimation of local genetic correlation may affect PRS performance when input GWAS have limited sample size. However, the summary statistics-based PRS combination strategy is robust in our analyses. In cases where there are concerns about the quality of local genetic correlation estimation, integrating summary statistics-based PRS combination into existing methodsTaken together, X-Wing addresses major challenges in existing PRS methods, showcases multiple innovations in trans-ancestry GWAS modeling, and substantially improves the prediction accuracy of PRS in non-European populations. These methodological advances, in conjunction with the ever-growing GWAS sample size especially in non-European populations, give hope to broad and equitable applications of genomic precision medicine around the globe.38 framework to detect genomic regions showing local genetic correlations between two ancestral populations. Suppose the association z-scores for two populations are denoted as Yk is a Nk-dimensional vector of standardized phenotype values with mean 0 and variance 1, and Xk is the standardized genotype matrix of dimension Nk × M where Nk is the GWAS sample size for population k. We define the scan statistic asR is the index set for SNPs in a genomic region, Σk is the variance-covariance matrix of zk and Σk,ii denotes the i-th diagonal element of Σk. We note that the Σk matrix can be estimated using 25, Vk is the LD matrix which can be estimated using a reference panel, θ controls the impact of LD. Technical details of the scan statistic and selection procedure for θ can be found in the Supplementary Methods.We extend the LOGODetectC controls the upper bound of the region size  and is pre-specified as 2000 in our analyses. Similar to local genetic correlation analysis in a single population38, we draw 5000 Monte Carlo simulations of z-scores for each population to assess the null distribution of Qmax, and we apply the scanning procedure to identify significant genomic regions showing cross-population local genetic correlations. Significant regions with a distance less than 100KB in-between are merged into a single segment.To perform statistical inference, we use the maximal scan statistic over all possible genomic regions as the test statistic:βk is a M-dimensional vector of SNP effect sizes in population k, ϵk is a vector of error terms with variance 69. MVN denotes multivariate normal distribution, and Ik is an identity matrix.Next, we describe our Bayesian PRS framework with annotation-dependent statistical shrinkage. Consider an additive genetic model:16, to employ variable degrees of statistical shrinkage for SNPs in different annotation categories45. Here we only consider one annotation for simplicity, but our model allows incorporating multiple annotations (Supplementary Methods). Consider an annotation with A categories, we assign an annotation-dependent horseshoe prior to βjk:We introduce an annotation-dependent shrinkage parameter, in addition to the global and local shrinkage parameters used in literatureβjk denotes the effect of SNP j in population k, ϕ is the global shrinkage parameter shared across all M SNPs and K populations, ψj represents the local shrinkage parameter for SNP j, λf(j),k denotes the annotation-dependent shrinkage parameter for SNP j in population k, j-th SNP to its corresponding category a in the annotation. The annotation-dependent shrinkage parameter is shared across SNPs that are in the same annotation category for a given population, but varies between populations to account for population-specific annotation.Here, k isDk is the LD matrix for population k.Given this prior and marginal least squares estimates Dk = I and the posterior mean effect for SNP j in population k isTo provide an intuition of annotation-dependent statistical shrinkage, suppose all SNP are unlinked , then the LD matrix λf(j),k tends to be big), the shrinkage factor Since SNPs in an important annotation explain more phenotypic variance (C+ (1) is the standard Cauchy distribution with the scale parameter equal to 1.To perform the full Bayesian model fitting, we assign half-Cauchy priors to the global, local, and annotation-dependent shrinkage parameters as follows:70. Following Ruan et al.16, we recommend using 1000 × K Markov Chain Monte Carlo (MCMC) iterations with the first 500 × K iterations as burn-in. We use the full Bayesian approach as default, which does not require validation data to tune the model. An alternative strategy is to select the optimal global shrinkage parameter ϕ from {10−6, 10−4, 10−2, 1} that maximized the R2 in the validation sample (Supplementary Methods)16. Our method outputs the posterior mean of population-specific SNP effects. PRS for the target cohort is calculated subsequently as the sum of allele counts weighted by posterior effect estimates.We employ a simple and efficient block Gibbs sampler to fit the PRS model using GWAS summary statistics and LD reference panel (Supplementary Methods)Below we explain how to incorporate annotations based on local genetic correlation in our PRS model. Without loss of generality, we assume population 1 is the target population. We break down our algorithm into three steps:k  to identify top s regions with positive local genetic correlation. We denote the set of regions as Ωk . We selected s = 1000 in our primary analysis and demonstrated that PRS performance is robust to the choice of s  to predict trait values in a different target population , Therefore, we want to amplify SNP effects that are more portable (i.e. correlated) between each non-target population and the target population. Second, we do not expect any portability issue when the GWAS population and the target population are the same . Thus, we do not employ any annotation-dependent shrinkage when estimating posterior SNP effects for the target population.λf(j), k)=1 for all j = 1, 2,… M, k = 1, …K. When estimating the posterior SNP effects for the non-target population k , we used λf(j),k = λk1, if SNP j is not annotated by Ωk, λf(j),k = λk2, if SNP j is annotated by Ωk, and K = 3 in the Supplementary Methods.Specifically, when estimating posterior SNP effects for the target population, we let k obtained in step2, we can calculate population-specific score PRSk. A common practice to combine these population-specific scores is to fit a regression model using the same phenotype Yv) individuals, First, we briefly illustrate that we do not need any individual-level data from the validation sample, and summary statistics is sufficient for estimating the least squares estimator Y(v) is the phenotype vector and PRS(v) is the N(v) × K matrix of K population-specific scores in this sample. Further, PRS(v) can be denoted as PRS(v) =X(v)b where X(v) is the Nv × M genotype matrix and b is the M × K matrix for SNP effects. For simplicity, we assume Y(v) is centered, X(v) is standardized, and b quantifies standardized SNP effects. We note that K population-specific PRS which can be approximated by the sample covariance obtained from a reference panel . Therefore, we haveb is obtained from the PRS training procedure. Nref)( and PRS(ref) denote the sample size and PRS matrix in the reference panel. Taken together, Eq. . This approach has three main steps which we describe below. Since this approach does not involve a separate validation sample, we will perform analysis using input GWAS from the target population , the sample size of which is typically sufficiently large to ensure the performance of repeated learning. Without loss of generality, we denote k = 1 for this (target) population.Typically, repeated learning  requires individual-level genotype and phenotype data since it involves sample splitting. Generalizing the technique in our recent workX1, Y1) into a training set (Nref).Suppose we divide the full GWAS sample (M × K matrix b with the k-th column showing standardized SNP effects for population k (Supplementary Methods).We train our PRS model using the training summary statistics subsampled for the target population in step1 and full GWAS summary statistics (without subsampling) for other populations. The output of PRS training is a We then estimate PRS weights byP-fold repeated learning. The final linear combination weights P times:p-th fold. To avoid overfitting, we used distinct reference panels from the target population for GWAS summary statistics subsampling, PRS model training, and estimating weights for PRS combination. We provide the equally divided reference panels from 1000G phase 3 data for Europeans, East Asians, Africans, Central/South Asians, and admixed Americans to the users. We also present the extensions of our approach to handle tuning parameters in PRS model, negative mixing weights from least squares, and multicollinearity between PRS in Supplementary Methods.At last, we perform 73 to simulate genotypes for 50,000 individuals of European and East Asian ancestry respectively from population-matched 1000 Genomes Project data. We only included SNPs with MAF greater than 5% on chromosome 22. After removing strand-ambiguous variants, 55,000 SNPs remained in the dataset and were used for subsequent analysis.We used HAPGEN248 (SNP heritability is dependent on LD and MAF), and binary trait scenario. In the heritability enrichment model, 30% of heritability was attributed to 1000 randomly selected SNPs and 70% of heritability to the remaining SNPs. LDAK model assumes that the effect size of the j-th SNP follows the normal distribution fj is MAF and uj is LDAK weight computed by the LDAK software. In the binary trait scenario, we first simulated the continuous liability following the same infinitesimal model as described above, then assigned the samples with top 50% liability as cases and others as controls. We repeated each simulation setting 100 times. Type I error rate was defined as the proportion of simulation repeats in which correlated regions (for X-Wing) and causal SNPs shared by two populations (for PESCA) were identified.First, we carried out simulations to assess the type I error rates of two methods . We generated the effect size of each SNP for two populations independently  following an infinitesimal model, where the per-SNP heritability was fixed as a constant. Trait heritability for two populations were set to be the same and varied between 0.001 and 0.01. We also compared two methods in three additional model settings: heritability enrichment model, LDAK model2) of X-Wing PRS with the existing methods PRS-CSx and XPASS (Supplementary Methods).Next, we compared the statistical power of X-Wing and PESCA under the heritability enrichment model. We randomly selected a genome segment on chromosome 22 spanning 1000 SNPs as the correlated signal region. We attributed 30% trait heritability to the signal region. We jointly simulated SNP effect sizes in the correlated signal region for two populations with a correlation set as 0.9, and then simulated effect sizes of the rest of the genome independently between populations. Trait heritability for two populations were set to be the same and varied between 0.001 and 0.01. We also investigated the LDAK model and the binary trait model. Each simulation setting was repeated 100 times. Statistical power was defined as the proportion of simulation repeats in which at least one identified region (for X-Wing) and one shared causal SNP (for PESCA) overlapped with the true signal region. We also performed simulations across the whole genome. We simulated genotypes for 50,000 individuals and 831,636 HapMap3 SNPs using the HapGen2 software. We simulated two independent traits for two populations under the infinitesimal model and assessed the type-I errors for the two methods. To compared statistical power under the heritability enrichment model, we randomly selected 50 genome segments, each spanning 1000 SNPs as the correlated signal regions. 30% trait heritability was attributed to the signal regions and 70% was attributed to the rest of the genome. Correlation of SNP effect sizes in the correlated signal regions was set as 0.9. We further performed simulation to compare the predictive accuracy . Trans-ancestry GWAS summary statistics for 13 traits were obtained from the PAGE study16 as LD reference panel and independent LD block provided by LDetect77 for block Gibbs sampler. When the target population is East Asian, we used UKB and BBJ GWAS as training data and European and East Asian LD reference panel. For the admixed American and African target population, we used UKB, BBJ, and PAGE GWAS as training data and European, East Asian, and admixed American LD reference panel, since PAGE GWAS consists primarily of Hispanic/Latino16. We randomly and evenly split the target cohort into a validation dataset to linearly combine population-specific PRS and used the remaining samples as the test dataset to evaluate PRS performance. When the PRS model involves model-tuning, the validation dataset is also used to select tuning parameters. We used partial R2 averaged across 100 random splits to benchmark the predictive accuracy of different methods, adjusting for age, sex, age2, age × sex, age2 × sex, and the top 20 genetic principal components. We used the percentage increase in partial R2 for X-Wing over other methods and reported the p-value from two-sided Wilcoxon signed-rank test to compare their performance. X-Wing uses local genetic correlation annotations based on genome-wide imputed SNPs in primary analysis but shows almost identical results using annotations based on HapMap3 SNPs  summary statistics for 75% BBJ (or PAGE study) samples as the training and the remaining 25% as the validation set. We applied X-Wing using the UKB and subsampled 75% BBJ training data  to obtain the posterior mean effects for each population. We then used these posterior mean effects to calculate PRS in the 1000G dataset for East Asian (or admixed American) samples and estimated the linear combination weights. We calculated the average weight values over four repeats, used these weights to combine population-specific PRS, and compared its prediction accuracy with the combined PRS based on individual-level data in the same target population. The weights selected from our repeated learning procedure for 29/31 traits in East Asians falls into the 95% confidence interval of the weights estimated in an independent sample (Supplementary Fig. We implemented 4-fold repeated learning to estimate the PRS combination weights using GWAS summary statistics and our equally divided 1000G reference panelImplementation details of XPASS, PESCA, PolyFun-pred, PolyPred+ and PRS-CSx are described in the Supplementary Methods.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-25Reporting Summary