Soybean is a valuable source of edible protein and oil, as well as secondary metabolites that can be used in food products, cosmetics, and medicines. However, because soybean isoflavone content is a quantitative trait influenced by polygenes and environmental interactions, its genetic basis remains unclear.2 population  was created by crossing the Korean cultivar Hwanggeum with low isoflavone contents  and the soybean mutant DB-088 with high isoflavone contents . A linkage map  with an average chromosome length of 152 cM was constructed using the 180K AXIOM® SoyaSNP array. Thirteen QTLs related to agronomic traits were mapped to chromosomes 2, 3, 11, 13, 19, and 20, whereas 29 QTLs associated with isoflavone contents were mapped to chromosomes 1, 3, 8, 11, 14, 15, and 17. Notably, the qMGLI11, qMGNI11, qADZI11, and qTI11, which located Gm11_9877690 to Gm11_9955924 interval on chromosome 11, contributed to the high isoflavone contents and explained 11.9% to 20.1% of the phenotypic variation. This QTL region included four candidate genes, encoding β-glucosidases 13, 14, 17–1, and 17–2. We observed significant differences in the expression levels of these genes at various seed developmental stages. Candidate genes within the causal QTLs were functionally characterized based on enriched GO terms and KEGG pathways, as well as the results of a co-expression network analysis. A correlation analysis indicated that certain agronomic traits  are positively correlated with isoflavone content.This study was conducted to identify causal quantitative trait loci (QTLs) associated with soybean isoflavone contents. A mutant-based FHerein, we reported that the major QTL associated with isoflavone contents was located in the interval from Gm11_9877690 to Gm11_9955924 (78 kb) on chromosome 11. Four β-glucosidase genes were identified that may be involved in high isoflavone contents of soybean DB-088. Thus, the mutant alleles from soybean DB-088 may be useful for marker-assisted selection in developing soybean lines with high isoflavone contents and superior agronomic traits.The online version contains supplementary material available at 10.1186/s12870-023-04519-x. The contents of all isoflavone forms were significantly influenced by genotypic effects (Table H2 ranged from 0.52 (GLE) to 0.98 (GNE), indicating that genetic factors have critical effects on soybean seed isoflavone contents. Overall, the observed transgressive segregation suggested that the alleles from both parental plants contributed to the isoflavone contents in the F2 population.The UPLC analysis revealed obvious differences between the isoflavone contents of Danbaek  and its mutant DB-088. The TI content was 4.04-fold higher for DB-088 .In contrast to Danbaek, DB-088 is characterized by indeterminate growth, resulting in delayed flowering and maturation, which makes cultivation and breeding challenging. Therefore, we attempted to increase the isoflavone contents of Hwanggeum, which has large seeds, matures relatively early, and exhibits determinate growth. Compared with DB-088 during the growth, maturity, and harvesting stages, Hwanggeum had superior agronomic traits, including FT, DM, GH, and HSW Table S. Seven q2 population , GH and PH , GH and NN , DF and DM , and DF and GH , whereas there were negative correlations between PH and LS , LS and NN , and HSW and SC . The analysis of the isoflavone contents indicated the TI content was highly positively correlated with the DZI , MDZI , GNI , MGNI , and ADZI  contents, which was in contrast to the negative correlation between the TI content and the AGNI content . Accordingly, the DZI, GNI, MDZI, MGNI, and ADZI contents contributed substantially to the TI content. The contents of three aglycones were highly correlated with the contents of the corresponding malonyl-glucosides . Notably, the AGNI content was negatively correlated with the contents of the other isoflavone forms, except for aglycones. Moreover, the GNI and MGNI contents were highly positively correlated with the DZI  and MDZI  contents, respectively.We performed a Pearson correlation analysis to evaluate the correlations among agronomic traits and between agronomic traits and isoflavone contents in the Fr = 0.34, P < 0.001), DM , and LW  had stronger positive correlations with the TI content. The average TI, DF, DM, and LW values for the bottom/top 20% of the F2 population were 1,655.72/5,279.66 μg g−1, 46.4/51.95 days, 127.25/151.05 days, and 7.29/8.65 cm, respectively. The differences were significant (P < 0.001).The analysis of the correlations between the agronomic traits and 12 isoflavone forms indicated DF and DM were positively correlated with the isoflavone contents, with the exception of the aglycone and AGNI contents. Furthermore, compared with the other agronomic traits, DF  were filtered using the following criteria: no missing data and monomorphic regions in the two parental plants and the Fon Table . Of the A QTL mapping analysis was also performed to reveal the causal factors associated with individual isoflavone and TI contents Table . A total−1) due to the Hwanggeum allele, which explained 20.1% of the PV. The three agronomic trait-associated intervals within Gm11_9955924–Gm11_11922317 overlapped the isoflavone-related intervals  were detected in at least two QTLs for isoflavone contents. The intervals on chromosome 11 were associated with the negative additive effect on the TI content (− 613.6 μg gP < 0.001) in the DB-088 seeds than in the Hwanggeum seeds at all developmental stages. Moreover, the Hwanggeum and DB-088 seed TI contents increased rapidly in the R6.5, but the TI contents in R6.5 and R7 were 3.12-fold and 3.7-fold higher in the DB-088 seeds than in the Hwanggeum seeds, respectively.The Hwanggeum and DB-088 seed TI contents were measured at five seed developmental stages Figure S. The incBGLU (β-glucosidase) genes, the BGLU13 (Glyma.11g129800) and BGLU17-1 (Glyma.11g129900) expression levels in Hwanggeum increased by more than 7-fold in R7, whereas the BGLU14 (Glyma.11g130000) and BGLU17 (Glyma.11g130100) expression levels in DB-088 tended to be highest during the early stages . The candidate gene expression levels differed significantly between the parental plants. The interactions between genes with similar expression patterns, which were revealed by the co-expression analysis, suggest the genes may contribute to the regulation of isoflavone contents and agronomic traits.The expression levels of four candidate genes within the major interval Gm11_9877690–Gm11_9955924 were analyzed in different seed developmental stages  interacted with four BGLU genes, namely Glyma.11g129500 (BGLU13), Glyma.11g129700 (BGLU17), Glyma.11g129300 (BGLU15), and Glyma.11g129600 (BGLU17), which were responsible for DF, GH, PT, and LL  and Glyma.11g129900 (BGLU17), which were associated with MGLI, MGNI, ADZI, and TI  in DB-088 ; the contents of the aglycones and conjugated forms were 3.7-fold/2.3-fold  and 4-fold/4.1-fold  higher in DB-088, respectively (Table SIsoflavones are polyphenolic compounds that are highly abundant in soybean . Soybean2 generation). By identifying causal QTLs with potential candidate genes, we revealed the potential applicability of these traits. The heritability of the contents of 12 isoflavones varied, from the relatively low heritability of the aglycone contents (35%–72%) to the higher heritability of the glucoside (63%–98%), malonyl-glucoside (72%–96%), and TI (44%–99%) contents  and DM [r =  − 0.16 (DZE) to − 0.37(GLE)]. Our findings are consistent with those of a previous study that showed the TI content can increase by up to 60.41% depending on the maturity time   and BGLU17-2 (Glyma.11g130100) tended to be highly expressed in the early seed developmental stages in DB-088, suggesting the encoded enzymes may function as glycosyltransferases (glycosylation) involved in synthesizing isoflavone conjugates. They are also likely related to the early flowering (by 10 days) and early maturation (by 20 days) of Hwanggeum. The positive correlation between specific traits (DF and DM) and the TI content  suggests the traits are associated with isoflavone accumulation. The co-expression analysis indicated that the gene–gene interaction was consistent with BGLU expression patterns (Glyma.11g129800 and Glyma.11g129900)  was associated with agronomic traits  and isoflavone contents . These findings suggest that these genes may have similar regulatory effects on isoflavone contents and agronomic traits.In Hwanggeum, DB-088 6,62 μg g−1DB-088 6,62 μg g−1BGLU genes on isoflavone accumulation in soybean seeds. The BGLU13 and BGLU14 amino acid sequences are respectively 98% and 87% similar to that of BGLU17, which belongs to a group of hydrolases that specifically target flavonoid and isoflavonoid conjugates [BGLU genes have been identified in Arabidopsis thaliana, Medicago truncatula, and Brassica rapa, respectively [BGLU6 is an acyl-glucose-dependent glucosyltransferase homolog that is co-expressed with phenylpropanoid biosynthetic genes; the mutation of this gene in the transgenic bglu6 mutant line decreases the flavonol 3-O-gentiobioside 7-O-rhamnoside (F3GG7R) content in accordance with the lack of F3GG7R associated with Ler BGLU6 genes. Our study findings were consistent with the results of previous studies showing that the total isoflavone content is approximately 4-fold higher in fully mature seeds (R7) than in immature seeds (R5) and green full-sized seeds (R6), with more than 80% of the TI content comprising glycosides and malonyl-glucosides. The glycosylation of isoflavones  leads to increased solubility and stability, which is conducive to transport and storage in vacuoles. Thus, the overexpression of BGLU13 (Glyma.11g129800) and BGLU17 (Glyma.11g129900) in Hwanggeum likely inhibits the formation and accumulation of isoflavone-glucosides in the late seed developmental stage because of the deglycosylation. Conversely, these genes are expressed at extremely low levels in DB-088, which enables glucosides to accumulate in the seeds.To date, there has been insufficient research on the effects of njugates , 76. Prenjugates . It cleanjugates , 79. A tectively , 80. Ishectively  determin2 mapping population derived from the soybean mutant DB-088 and a high-density SNP chip array. The QTLs identified in this study included novel QTLs as well as previously reported QTLs. In the major interval on chromosome 11, we identified the following candidate genes associated with favorable agronomic traits and high TI contents: BGLU13 (Glyma.11g129800), BGLU14 (Glyma.11g130000), and BGLU17 (Glyma.11g129900 and Glyma.11g130100). The study results may be useful for future research aimed at developing molecular markers applicable for the breeding of new soybean lines with optimized isoflavone contents and agronomic performance.We mapped the causal QTLs underlying agronomic traits and isoflavone contents using an FAdditional file 1: Figure S1. Morphology of plants and their seeds. From left to right: Danbaek, DB-088, and Hwangguem. (A) Plants. (B) Seeds.Additional file 2: Figure S2. Distribution of agronomic traits in the F2 population. Growth habit ; Plant type ; Leaf shape ; Pod color ; Seed color ; Hilum color . The red and blue dotted lines indicate Hwangguem and DB-088, respectively. The green line indicates the average for the F2 mapping population.Additional file 3: Figure S3. Construction of a high-density linkage map in 20 chromosomes. The SNP position and genetic distance are provided on the right and left sides, respectively.Additional file 4: Figure S4. Analysis of QTLs related to agronomic traits using a high-density linkage map of 20 chromosomes. The SNP position and genetic distance are provided on the right and left sides, respectively. The QTLs are positioned to the right of the chromosome. The QTLs in the same color are in identical positions on the chromosome.Additional file 5: Figure S5. Total isoflavone content in seeds of Hwanggeum and DB-088 according to developmental stages. *, **, and *** indicate significance at p < 0.05, 0.01, and 0.001, respectively.Additional file 6: Figure S6. Enriched GO term and KEGG pathway analyses of candidate genes associated with agronomic traits. Nodes indicated a specific term and are functionally linked based on term-term similarity. Each different color indicates the term enrichment, and only the most significant term is shown. The colors of the bars and nodes match according to respective terms. The node size means significance and group color are arbitrary. The percentage of genes per term and terms per group are shown in bars label and pie chart, respectively. Figure adapted with copyright permission from the Kyoto Encyclopedia of Genes and Genomes.Additional file 7: Figure S7. Gene-gene interactions network of candidate genes associated with agronomic traits. Edges (gray line) indicate gene-gene interactions and node (gene) colors are arbitrary. Higher confidence scores are represented as thicker and darker lines.Additional file 8: Table S1. List of qRT-PCR primers.Additional file 9: Table S2. Total and individual isoflavone contents in seeds of Danbaek, Hwangguem, and DB-088.Additional file 10: Table S3. Descriptive statistics of agronomic traits in parental plants and F2 population.Additional file 11: Table S4. QTL analysis for agronomic traits in the F2 population.Additional file 12: Table S5. Information of GO terms for agronomic tratis.Additional file 13: Table S6. Information of GO terms for isoflavone contents.


Genomic selection is expected to improve selection efficiency and genetic gain in breeding programs. The objective of this study was to assess the efficacy of predicting the performance of grain sorghum hybrids using genomic information of parental genotypes. One hundred and two public sorghum inbred parents were genotyped using genotyping-by-sequencing. Ninty-nine of the inbreds were crossed to three tester female parents generating a total of 204 hybrids for evaluation at two environments. The hybrids were sorted in to three sets of 77,59 and 68 and evaluated along with two commercial checks using a randomized complete block design in three replications. The sequence analysis generated 66,265 SNP markers that were used to predict the performance of 204 F1 hybrids resulted from crosses between the parents. Both additive  and additive and dominance (full model) were constructed and tested using various training population (TP) sizes and cross-validation procedures. Increasing TP size from 41 to 163 increased prediction accuracies for all traits. With the partial model, the five-fold cross validated prediction accuracies ranged from 0.03 for thousand kernel weight (TKW) to 0.58 for grain yield (GY) while it ranged from 0.06 for TKW to 0.67 for GY with the full model. The results suggest that genomic prediction could become an effective tool for predicting the performance of sorghum hybrids based on parental genotypes. In sorghum hybrid breeding, populations are initiated from crosses between selected parental sources, and segregating populations are evaluated over multiple seasons, and most promising inbred lines are selected as potential parents often based on their performance in hybrid combination with other lines. Promising female parents undergo conversion into cytoplasmic male sterility before they can be tested in hybrid combinations. Development of hybrid cultivar is a cumbersome process; it involves synthesis of hundreds of testcross hybrids and evaluation over multiple environments to identify handful of most promising hybrids. It takes significant amount of time and resources to complete the development of hybrid product.The advent of molecular marker techniques has opened a new horizon for enhancing breeding efficiency through reducing time needed to develop cultivars or improving accuracy during selection . Marker-Therefore, a less expensive and faster method that allows selection of inbred parents with enhanced hybrid performance is needed. Such method should provide a clue about how the most promising hybrids can be identified without expensive and laborious field testing. Since hybrid performance is the result of putting together of different alleles at several loci associated with the trait of interest , new metThe next generation sequencing (NGS) technologies have provided tools for scanning the entire genome of species instead of few selected genomic regions and capture single nucleotide polymorphisms (SNPs) throughout the genome. Such polymorphisms are often in linkage disequilibrium with alleles responsible for a change in gene functions. Thus, selection approach that takes into account all SNPs across the genome known as genomic selection (GS) may be more powerful than other indirect selection schemes used in the past. Genomic selection is a modified version of MAS that predicts the genetic values of individuals using genome-wide markers without the need for gene and QTL discovery. Unlike MAS, GS permits the use of molecular markers with both major and minor effects on the traits to build the prediction model that is used to predict the phenotypes of untested individuals . PhenotyGenomic selection has been successfully conducted in several crops . When thIn sorghum, GS studies were mainly focused on model training to predict genomic estimated breeding values (GEBVs) of individuals in different sets of populations . Grain y22.1A total of 102 public parental inbred lines, including 99 pollinator lines (fertility-restorer lines) and 3 seed parents , bred at Kansas State and Texas A&M Universities, were used in this study. Of these, 59 lines were Acetolactate synthase (ALS) inhibitor herbicide-resistant sorghum pollinator parents (R-lines), 16 were Acetyl co-enzyme-A Carboxylase (ACCase) pollinator parents and 24 were conventional (non-herbicide resistant) pollinators. The lines represented diverse pedigrees in the program and were believed to provide diverse set of hybrids when crossed with three tester females that also represent diversity among the public female inbreds. The female parents were ATx399, ATx3042 and AOK11. A total of 204 F1 hybrids developed from crosses between 99 pollinator lines and the three seed parents were categorized into three subgroups. Group 1 hybrids consisted of crosses between 77 pollinator parents and AOK11 as a female parent, while Group 2 comprised hybrids from crosses between 59 pollinator parents and ATx3042. Group 3 comprised F1 hybrids between 68 pollinator parents and ATx399. Forty-four of the pollinator lines were common across the three populations.2.2The 204 F1 hybrids were evaluated across four environments at Kansas State University (KSU) Agronomy Research Farm Ashland Bottoms near Manhattan during 2012, 2013 and 2014 seasons and at the Northeast experimental station near Ottawa, KS during 2014. The tests at Ashland bottoms were planted on June 8, 7 and 17 for 2012, 2013 and 2014 seasons, respectively. Field planting at Ottawa was done on June 17, 2014. The experiments were laid in a randomized complete block design with three replications. The gross plot size was 5 m long paired rows spaced 0.75 m apart. On average, the annual precipitation for KSU Agronomy Research Farm Ashland Bottoms was 338, 539 and 576 mm for 2012, 2013 and 2014, respectively.Data were collected on days to flowering, plant height, grain yield and yield components, including panicle length, panicle weight, panicle yield, number of kernels per panicle, and thousand kernel weight. Days to flowering was determined by recording the number of days from planting to when 50% of plants in each plot reached half-bloom. The plant height was recorded by measuring the distance from soil surface to the tip of the panicle at physiological maturity expressed in centimeters. The grain yield was measured as the weight of the kernels harvested at maturity from each plot recorded in kilograms per hectare.Prior to harvesting, three panicles from main plants were randomly sampled from each plot for measuring yield components. Mean of the three panicles was used to represent a plot and the moisture content was adjusted to 12.5% for statistical analysis. The panicle length was determined as the mean length of the panicles measured from the base to the tip of the panicle. The panicle weight was recorded as the weight of panicle from individual plant. The panicle yield was measured as the weight of grains threshed from a single panicle. The kernel number was recorded by counting the kernels threshed from each panicle using a laboratory seed counter . The thousand kernel weight was determined by measuring the weight of 250 kernels from each panicle and multiplied by four.2.3https://www.biotech.cornell.edu/core-facilities-brc/facilities/genomics-facility) as described in http://phytozome.jgi.doe.gov/pz/portal.html; http://www.maizegenetics.net/tassel/; Seeds of the parental lines were planted in the greenhouse at Kansas State University using 96-cell flat trays filled with Metro-mix 360  growing medium. Two weeks after planting, young leaf tissues were harvested from each line for genomic DNA extraction using the standard cetyltrimethylammonium bromide (CTAB) method . The Qua2.42.4.1The variance components were calculated using SAS v.9.3 . The folyijk is the phenotypic observation for ith single cross evaluated in the jth environment, μ is the grand mean for a trait; gi represents effect of the ith single cross; ej represents the effect of the jth environment; (ge)ij represents the interaction effect between single cross and environment; rk(j) represents the effect of replication nested within the jth environment; and eijk represents the residual variance. Environment and replication nested within environment effects were modeled as fixed effects while all other effects were treated as random. Error variance was allowed to be heterogeneous among environments.where Broad-sense heritability (H) for each trait was estimated across environments as described by r is the number of replications and e is the total number of environments.where 2.4.2To account for population structure that affects prediction accuracy , we comp2.4.3Genomic estimated breeding values (GEBVs) were calculated using ridge regression best linear unbiased prediction (RR-BLUP) model implemented in rrBLUP package in R , which aPrediction accuracy, r , was computed as a measure of the correlation between the observed and predicted phenotypes and divided by the square root of heritability of the trait across environments . Single-33.1-1, respectively. Overall, each hybrid flowered 65 d after planting, was 111cm tall, and produced 7.9 Mg ha-1 grain yield. Mean panicle length, panicle weight and panicle yield were 25.5 cm, 68.8 g and 47.7 g, respectively. Mean kernel number per panicle and thousand kernel weight were 1,640 and 29.1 g, respectively. Broad-sense heritability varied from 0.23 for grain yield, thousand kernel weight, and panicle weight to 0.81 for flowering time.3.2The first three PCs from the PCA computed across the 102 parental lines accounted for 25.1% of the variance. A plot of PC1 (11.6%), PC2 (7.5%) and PC3 (6.0%) revealed three groups, which generally agrees with pedigree information of the maternal lines Figure 13.33.4Prediction of hybrid performance was studied for various TP sizes considering additive marker effects alone as well as for combined additive and dominance effects, the results are summarized in Prediction accuracy of hybrid performance using five-fold CV where TP and VP are related by common males or females using the partial model are presented in 4The recent breakthrough in genetic marker technology and bioinformatics tools integrating DNA markers with phenotypes has expanded the knowledge of marker effect on phenotype; opening way for MAS to enhance breeding efficiency. While the applicability of MAS was limited to QTL with large effect, a further development based on next-generation sequencing has provided a more powerful tool, genomic selection (GS), to facilitate selection for small effect QTL affecting key traits of agronomic importance . Because-1) observed in this study may be partly the result of increased heterosis that resulted from the low kinship coefficients among the lines.In the present study, GS was used to predict F1 hybrid performance with respect to eight different agronomic traits of sorghum. Prior to building the genomic prediction model, structure analysis was conducted to determine population structure and familial relatedness. The kinship values among the lines were expectedly low and it may be the result of a deliberate attempt by the breeding programs to diversify parental sources in order to maximize hybrid vigor. The grain yield values  to predict the performance of the selection candidates based on their genotype only. For GS to be effective, it is very important that high quality genotype data is obtained on the entire population and good quality phenotype data on the TP. This study also looked at the effect of TP size on prediction accuracy of hybrid performance and compared two prediction models, one based on additive marker effects only, and the other considering both additive and dominance effects, to predict F1 hybrid performance in sorghum. The additive and dominance allelic effects were estimated for each marker and used to calculate predicted phenotypes (GEBVs) for untested F1 hybrids using RR-BLUP genomic prediction based on an infinitesimal model where all predictors are maintained in the analysis. This model gave higher prediction accuracies in previous studies .Previous studies have shown that in cross-validation schemes, prediction accuracy can be overestimated if both TP and validation population (VP) sets contain related lines . TherefoIn this study, prediction accuracy was markedly different for different traits with grain yield having more than 50% accuracy and thousand kernel weight consistently the lowest. Increase in TP size improved prediction accuracy for all traits but the extent of the increase was different for different traits. Similar results have been reported in previous studies in other crops . Jan et Again, grain yield consistently had the highest five-fold CV prediction accuracy among the traits assessed in this study. This result corroborates previous studies that have also reported high prediction accuracy of grain yield in wheat  and biomThis study has shown that it is possible to predict the performance of untested sorghum hybrids for important agronomic traits such as grain yield solely based on the genotype information by using a genomic prediction model. Thus, GS may become a viable tool for predicting the performance of sorghum hybrids prior to committing resources for expensive phenotyping. This intern may help to significantly reduce the number of hybrids to be evaluated and costs associated with phenotyping a large number of hybrids in the field. The fact that genotyping and sequencing costs have been decreasing and knowledge of computational biology expanding, it is becoming possible that public breeding programs can affordably deploy genomic selection platforms to add efficiency and reduce the overall cost of developing a hybrid technology.https://github.com/framau2023/SNP_marker_data_GP.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. The data presented in the study are deposited in the github repository, accession number TT conceived the work, acquired funding support for the project and provided supervision, and edited the draft manuscript. FM conducted field experiment, data analysis preparation of the draft manuscript. RP and DS co-edited the manuscript. All authors contributed to the article and approved the submitted version.


For years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a “combined” SS (CSS) evaluation that extends the “combined” BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits  is added.GEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.A CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a “combined” evaluation of functional traits is more modest for these males.The online version contains supplementary material available at 10.1186/s12711-023-00839-6. Genetic evaluations using pedigree and phenotypic information have been used for decades to estimate the breeding values of animals. However, low-heritability traits are difficult to evaluate accurately. Moreover, in some cases, phenotypes that are recorded late in life delay the time when the estimated breeding values (EBV) are accurate enough. One way to increase the accuracy of EBV for these traits is to develop a multiple trait evaluation, which consists in taking advantage of the information from traits that can be recorded early in life and are correlated to the trait of interest. The information brought by the correlated traits enhances the accuracy of the evaluation of all the traits of interest. However, in some cases, simple multiple trait evaluations are not possible. Indeed, different correlated traits can be described by very different models: for instance, it is often important to consider the known sources of the heterogeneity of genetic and/or residual variances for some traits—e.g. for production traits , 2 or tyFunctional longevity (FL) is an important trait, which is receiving more and more attention as a relevant breeding objective to increase both the productive life and welfare of dairy cows . HoweverThe inclusion of genomic information in genetic evaluations has led to an increase in genetic trend because own animal performances were no longer required to obtain accurate genomic (G) EBV . GenomicIn recent years, single-step (SS) approaches, which consider simultaneously all the information available for all animals , have been developed to estimate GEBV. They reduce the bias in genetic trends and thus in GEBV and they increase the accuracy of GEBV by including information from ungenotyped animals –17. HoweThe aim of this study was to illustrate the benefits of a “combined” single-step (CSS) evaluation that can aggregate information from correlated traits –6. TheseFunctional longevity is difficult to predict with high accuracy because the true phenotype (length of productive life) is known only when the animal is dead. Many studies have shown that other routinely recorded traits are significantly correlated with FL, e.g. udder type traits , 23, ferRecords for the six traits mentioned above and pedigree information for the Montbéliarde cows used in this study were obtained from the French bovine national database , 28. Thecumulative hazard function during the productive life of the cow represents an aggregate measure of its risk of being culled, which is considered as a proxy of FL [Udder depth scores, ranging from 1 (very deep udder) to 9 , were collected by Montbéliarde breed technicians. Conception rate for heifers and CRC were based on the dates of artificial insemination (AI) and of the next calving of each cow. These are repeated traits representing the success (1) or failure (0) at each AI. Somatic cell score and CM were collected during monthly milk recording. Somatic cell score was expressed as the logarithm of the number of somatic cells per mL of milk as measured at each milk sampling test. Clinical mastitis indicated the presence/absence (1/0) of at least one mastitis event of a cow during lactation. Pseudo-records for FL were obtained after a routine genetic evaluation in France , which uxy of FL . The genTo contrast the accuracy of univariate and combined SS evaluations, an evaluation at the beginning of 2016  was compared to the evaluation with all the data available in 2021. The elementary statistics of records for each trait are in Table The performances in Table Recent studies have included genomic information to re-estimate the genetic parameters and in particular the genetic correlations between traits, and it has been recommended to use all available genomic information for the genomic evaluations , 31. ForCovariance components were estimated using the following multiple trait sire model (with 6 traits):t method . Allele t method .Genetic correlations were calculated as:The heritability of each trait was computed as the ratio between the genetic and phenotypic variance. Genomic correlations and heritabilities obtained for the six considered traits are in Table As expected, some correlations between traits are relatively high . In Table The hybrid single-step (HSS)GBLUP software  is basedThe model used to describe the phenotypes for an ungenotyped animal is:For a genotyped animal, the model is:At convergence, the GEBV were calculated as follows: same as  above, aThe underlying principle of a “combined” genetic evaluation  was adapAfter the univariate evaluations step, for repeated traits, pseudo-records were computed as described above. Pseudo-records for each trait were used in the second step of the CSS evaluation with the following multiple trait model for all animals:where  for Eq.  and \docThe objective of adding a year effect (ye) was to correct for potential selection bias that exists in the first step of the CSS evaluation where correlations between traits were ignored , 37, 38.With the objective to illustrate the benefits of a CSS evaluation, it was performed including all the information available for all the animals . For example, the first group of genotyped cows included the top 10% genotyped cows based on their GEBV for FL in 2016. Four separate cases were considered: GEBV from the USS and CSS evaluations in 2016 and for genotyped and ungenotyped heifers. These different cases were used to better visualize the quality of the different evaluations for FL, depending on the evaluation strategy used . For each of these four combinations, an average survival curve was calculated for each decile, using the Cox model of the Survival Kit software (v6.12) . The CoxMoreover, in order to check the quality and impact of the within-herd standardization of FL for milk production with the current genetic evaluation using a Weibull sire model, the average milk records in first lactation after correction for lactation length were calculated for the cows grouped by GEBV deciles for FL separately for univariate and “combined” SS evaluations in 2016. By construction, it was expected that breeding values for FL were not influenced by the level of production.In France, genomic evaluations have been used since 2009 in the three major dairy breeds (including the Montbéliarde breed) . An inteFor each trait and for each of the three subpopulations , Table Yearly correlations between the GEBV for FL obtained with the univariate evaluation and those obtained with a combined evaluation without performances for FL were also calculated for the females born between 2010 and 2019  but the gap between curves was reduced for the best groups of females, and particularly for the best females that were evaluated with the CSS evaluation  and with a very strict convergence criterion . The USS evaluations took about 2–7 h to reach this criterion, depending on the traits. The corresponding time for the CSS evaluation was about 15 h. CSS evaluations  improve the accuracy of GEBV and particularly for FL. This study considered the Montbéliarde breed which is the second largest dairy breed in France , 51, or In spite of these limitations, it appears that the proposed CSS evaluation provides a clear improvement of the predictability of FL, for both genotyped and ungenotyped cows. This would benefit to both the breeding schemes, which could improve the selection of bull dams on this trait, and the farmers for the choice of their replacement heifers. A similar approach could be applied in other similar situations. For example, this is the case for carcass traits  in dual-purpose breeds that could be combined altogether and with some conformation traits, such as the height of sacrum or chest width of cows, for example .One of the anonymous reviewers indicated that extra validation of level and dispersion biases would have been possible by considering the correlations between GEBV for FL obtained with and without performances as displayed in Figs. The proposed strategy to implement a “combined” single-step evaluation for functional longevity and correlated predictor traits showed very promising results in the case of the Montbéliarde breed. In particular, we showed that more accurate (G)EBV of both genotyped and ungenotyped heifers facilitate the selection of more robust cows, with a longer predicted productive life. The impact of such an approach is less visible on the selection of bulls, because the selection intensity of males for functional traits is already strong. The combined single-step approach can be extended to any complex phenotype resulting from elementary traits that are described by models that are too different to be considered directly in a multiple trait analysis.Additional file 1: Table S1. Mean observed difference in days of productive life between the extreme groups of heifers (worst and best deciles) at different stages of productive life. The data presented were derived from the survival curves obtained using a Cox model. The mean differences in days between extreme groups of heifers were calculated for the USS and CSS evaluations. The results are separated according to the genotyping status of heifers.


Mechanisms of inheritance remain poorly defined for many fitness-mediating traits, especially in long-lived animals with protracted development. Using 6,123 urinary samples from 170 wild chimpanzees, we examined the contributions of genetics, non-genetic maternal effects, and shared community effects on variation in cortisol levels, an established predictor of survival in long-lived primates. Despite evidence for consistent individual variation in cortisol levels across years, between-group effects were more influential and made an overwhelming contribution to variation in this trait. Focusing on within-group variation, non-genetic maternal effects accounted for 8% of the individual differences in average cortisol levels, significantly more than that attributable to genetic factors, which was indistinguishable from zero. These maternal effects are consistent with a primary role of a shared environment in shaping physiology. For chimpanzees, and perhaps other species with long life histories, community and maternal effects appear more relevant than genetic inheritance in shaping key physiological traits. In wild chimpanzees, shared community and non-genetic maternal effects are determined to be a more powerful force compared to genetic inheritance in shaping urinary cortisol levels. As a consequence of this multi-faceted and dynamic role, the regulation of HPA axis activation and GC secretion is of broad interest to ecologists and evolutionary biologists seeking to understand how animals adapt to changing environments7. Despite the flexibility of HPA axis activity in response to external and internal stimuli, numerous studies demonstrate consistent individual differences in HPA axis activity and reactivity to environmental stimuli8. Inter-individual variation in HPA axis regulation is linked to variation in immune function and can be predictive of variation in fitness outcomes11. For example, in both baboons and gray mouse lemurs, individuals with consistently elevated HPA axis activity have poorer survival outcomes and live substantially shorter lives than those with lower HPA axis activity12. Given the profound fitness effects of individual differences in HPA axis activity and regulation, understanding the relative role of genetics, experience, and environment in shaping these GC phenotypes is key to deciphering the evolution of physiological plasticity13.In vertebrates, glucocorticoids (GCs) such as cortisol, secreted via the hypothalamic-pituitary-adrenal (HPA) axis, facilitate homeostasis via mediation of metabolic, immune, and behavioral responses to intrinsic and extrinsic stressors17. While twin studies in humans have been important in revealing the genetic regulation of cortisol levels, they are often limited due to issues related to short-term sampling20 and the restricted information available on the individual- and environmental-level factors that can become conflated within genetic effect estimates in human research22. Where the relative contributions of genes and environment have been assessed in relation to variation in cortisol levels or other health-related factors in humans, researchers often find a greater influence of shared family environments as compared to genetics (e.g. ref. 20). However, these human studies are still often not able to delineate how much these familial effects are a product of parental effects or the macroenvironment in which families are situated, e.g. broader socioeconomic environments22.Results from human twin studies indicate that as much as 60% of the variation in cortisol levels may be explained by genetic effects27. Like the aforementioned human studies, the animal literature suggests genetic inheritance plays a clear role in the formation of GC phenotypes, with, on average, 30% of the variation in cortisol patterns explained by genetic effects in these studies27. However, this work has largely focused on short-lived species27; . Therefore, beyond human studies, we know comparatively little about how GC phenotypes emerge and are maintained in other long-lived species with protracted parental care. Determining the relative contributions of genetics, the microenvironment of parental effects, and the broader socioecological macroenvironment to variation in GC phenotypes is an important topic in evolutionary ecology. It can help us to understand the evolution of protracted development as a life history adaptation and the importance of non-genetic parental effects and plasticity within that extended ontogenetic phase.Non-human animal  studies are less constrained than most human studies as researchers can either use experimental procedures, for example cross-fostering, or include appropriate environmental variables in their modeling to better account for shared environmental effects28, including the regulation of cortisol and other GC levels33. In experimental rodent studies, maternal cortisol levels during pregnancy and during post-partum offspring rearing, as well as rates of maternal interaction with offspring, are all predictors of offspring cortisol levels and reactivity34. Rodent studies also suggest that maternal effects may occur via epigenetic processes, such as DNA methylation of GC receptor promoter regions, leading to altered responsivity to stressors34. Primate studies of the role of maternal effects on cortisol secretion and reactivity have typically employed maternal deprivation paradigms, either via experimental separations or due to naturally occurring maternal loss33. Here, maternal loss is linked to elevations in cortisol levels or alterations to diurnal rhythm30, however, these effects do not necessarily last into adulthood33. Similarly, in human studies, tests of maternal effects on cortisol regulation classically examine the consequences of negative maternal or early life circumstances ). Here, maternal loss or early life adversity related to maternal condition are associated with elevated HPA activity in offspring, which can last into adulthood for some individuals. Therefore, much of what we know about maternal effects on cortisol regulation in long-lived mammals is derived from studies of manipulated and/or extreme maternal circumstances.Parental, and especially maternal, effects are recognized as major evolutionary drivers of phenotypic trait variation35, and a prolonged immature dependency lasting at least 10 years36, in which there is emerging evidence of maternal influences in growth, social development, survival, and future reproductive success40. Chimpanzees live in societies with a high degree of fission-fusion dynamics41, and adults have relatively stable social phenotypes across years42, with certain adult females, and thus mothers, consistently more gregarious and social than other individuals43. Maternal social characteristics are thus likely to be a key factor shaping the social environment of their offspring prior to adulthood. Therefore, during both pre- and post-natal phases, there is a long period in which maternal factors can shape endocrine phenotypes that may endure throughout adulthood in chimpanzees.In our study, we investigate the relative contributions of environmental, genetic, and non-genetic maternal effects  to variation in cortisol phenotypes in wild chimpanzees. Chimpanzees are a long-lived species, have a gestation period of approximately 8 monthsPan troglodytes verus and eastern, Pan troglodytes schweinfurthii), we applied well-established methods and mixed-effect models to partition variance and calculate the repeatability and heritability of cortisol levels in wild chimpanzees45. Repeatability coefficients allow us to quantify the level of individual variation in cortisol levels and thus whether it could be subject to selection44, while heritability estimates indicate the relative contributions of environmental, parental and/or genetic factors in generating individual variation in this trait45.Using a dataset of 170 adult and immature individuals of both sexes from five different communities and two subspecies  and in plasticity to the environment (reaction norm slope)50. Thus, heritability estimates of reaction norm intercepts and slopes reflect the relative contributions of environmental, parental and/or genetic factors in shaping average responses and plasticity to the environment respectively52. It is important to note that heritability estimates only reflect environmental/genetic/parental contributions to among-individual variation, i.e. a trait can have a genetic basis, for example, and a heritability estimate of 0 if the trait lacks variation in a population53.Trait repeatability is defined as the proportion of variance in a trait, here cortisol levels, explained by consistent individual differences55. Across numerous chimpanzee cortisol studies, time of day (i.e. circadian rhythm) is one of the most consistent environmental predictors of cortisol levels58, making it a promising reaction norm to investigate in terms of repeatability and heritability. In our study, we take a two-step approach, first establishing repeatability in circadian reaction norms, then examining heritability contributions to individual variation in these reaction norms. Chimpanzees are an interesting study species for this topic; although sampling across the lifespan is challenging for this long-lived mammal, many of the socioecological factors influencing variation in cortisol levels in this species are established62. Therefore, we can account and control for these factors in our statistical models, providing robust estimates of the relative contributions of genetic, maternal, and environmental effects to cortisol levels and circadian reaction norms in a wild, long-lived mammal with prolonged development and maternal care.In chimpanzees, as well as humans, the circadian cortisol response is a well described reaction norm: levels rise gradually during sleep prior to a peak upon awakening, followed by declines throughout the day40, coupled with evidence of genetic regulation of circadian cortisol responses in humans17, we anticipated both genetic and non-genetic maternal effects to make substantial contributions to variation in cortisol patterns in wild chimpanzees.Given the prolonged maternal dependency observed in chimpanzeesn = 6123 samples), we quantified cortisol levels using liquid chromatography-tandem mass spectrometry  and corrected for variation in water content in the urine using the specific gravity (SG) of each sample64. Therefore, we report urinary cortisol levels as ng cortisol/ml SG. From the fecal samples, we genotyped DNA extracts using a two-step amplification method including 19 microsatellite loci (per65).Urine and fecal samples were collected from individuals of all life stages (2–53 years old) from five chimpanzee communities. For each urine sample , we included 170 individuals from this pedigree in our final dataset. Table 66. Our key predictor of urinary cortisol concentrations  was time of day, which we converted into a continuous, hours-since-midnight value for each sample.We used linear mixed-effect models (LMMs) with a Gaussian error structure to test adjusted repeatability in urinary cortisol concentrations, i.e. the proportion of variance attributable to between-individual differences given conditional effectsnull model included only the following random effects: a factor variable to account for variation in socioecology within groups (“group”), a factor to account for temporal variation within groups composed of group identity and the sampling year (termed “group-year”), and a variable to account for samples being pooled from various research projects (“project identity”). For the latter, although all projects follow the same protocol for sample collection and analysis, different projects may have different priorities for the individuals or contexts sampled, therefore, this random effect was included to account for such biases.For our repeatability analysis, we fitted three models with identical responses and fixed effects but varying random effect structures . Lastly, the individual reaction norms model was identical to the individual intercepts model other than it included random slopes for the time of day within the random effects of individual identity and ID-year. Previous research found higher repeatability for the quadratic term of time of day in our study populations49; therefore, we included time of day as both linear and quadratic terms to model the potential circadian responses.The In all these repeatability models, we included as fixed effects variables previously shown to influence urinary cortisol levels  or random slopes for linear and quadratic effects of time of day  improved the predictive power of each model. This would indicate whether individual differences in average cortisol levels or individual differences in cortisol plasticity to time of day, respectively, explained variation in cortisol levels well. We found strong support for the inclusion of the random intercepts for individual identity, but weak support for the inclusion of random slopes within-individual identity: in most model comparisons, the intercepts only models were preferred to the reaction norm models , from the individual reaction norms model, we calculated a within-year (i.e. short-term) trait repeatability estimate (variance explained by the ID-year variable) of 0.07 . The model structures were identical for each demographic with the exceptions that a continuous variable for dominance rank was included in the male model, a categorical variable (lactating or cycling) for reproductive status was included in the adult female model, and a categorical variable indicating sex was included in the immature model . In the Supplementary Materials Figs. individual reaction norm models of each demographic. For all demographics, we still observed a high amount of uncertainty for our reaction norm repeatability slope estimates , we also included the identity of the mother of the individual sampled as a random effect. For this model, we also included random slopes for the linear and quadratic terms of time of day within all random effects (including project identity and group-year).We estimated the heritability of urinary cortisol levels and circadian cortisol responses by implementing the well-established “animal model” approachFull heritability model; 6123 samples from to 170 individuals). Second, to ensure any environmental or genetic effects were not an artifact of pooling samples from different populations and subspecies of chimpanzees, we fitted a model only including individuals sampled in the Taï forest . Lastly, in male and female chimpanzees respectively, there tends to be a positive and negative correlation between dominance rank and GC levels69, which should ideally be included in models to accurately model individual variation in cortisol levels. However, due to limited behavioral data for certain individuals (see “Methods”), we could not assign ranks to all individuals in the dataset. Therefore, we ran a third heritability model including only individuals to which we could assign ranks .To ensure that we could effectively model heritability with the data available to us, we fitted three main different animal models. First, we fitted a full animal model using all the urinary cortisol values available to us , maternal effects , and shared community effects (variance explained by the group variable). In addition to these factors related to heritability, we also calculated the proportion of variance attributable to temporal within-group factors (variance explained by the group-year variable), individual identity over the long-term , and the short-term (variance explained by the ID-year variable). For all these factors, we calculated the proportion of variance they explained for each component of the circadian reaction norm, i.e. average cortisol levels (intercepts) and cortisol responses to the linear and quadratic terms for time of day (sensu52). We also estimated the proportion of covariance between intercept, linear slope, and quadratic slopes explained by all these factors. Finally, we implemented a variation of the Full heritability model, referred to as Trait heritability model, in which the linear and quadratic circadian slopes were not present so that heritability could be calculated including the residual variance52.For each of these models, having accounted for the influence of the fixed effects  of the average cortisol variation, while the genetic contribution was distinguishable from zero.The relative contribution of our random effects to variation in circadian cortisol responses in wild chimpanzees from the Taï and Dominance heritability models , only one permutation out of 100 had estimates lower than the observed data Fig. , while fFull heritability model  than that identified using our observed data and the del Fig. . Similarls Figs.  and S11,el Table –14, suppWe conclude that the maternal environment is more influential than genetics in shaping cortisol responses in wild chimpanzees, while shared community effects are the clearest influence on this specific phenotype overall.Our study leverages up to 18 years of long-term data collection of more than 6000 urine samples from 170 individuals to identify consistent individual differences in circadian cortisol responses in wild chimpanzees. Using this unique dataset, we find that group-wide, socioecological effects, represented by the “group” random effect in our models, have the most pronounced influence on variation in cortisol levels in this species. These effects held even when examining contiguous chimpanzee communities. Despite this predominant impact of socioecology, we were able to discern a significant role for the maternal environment in shaping average cortisol levels among individuals, with limited evidence for genetic effects alone driving individual differences. Our results were robust to different model structures and are corroborated by permutations of the data which indicate our maternal and genetic effect estimates are not artifacts of group structures. Further, the shared community effects cannot be explained by pooling samples from different populations and subspecies of chimpanzee, nor by established group-level predictors of cortisol such as group size or sex ratios. Our study shows the importance of long-term data collection in the wild, especially for long-lived species with protracted maternal care. It also raises important biological questions about the nature of both the macroenvironmental shared community effects and microenvironmental, non-genetic maternal effects that we documented, which combined accounted for virtually all variation, conditional on our fixed effect structure, in average cortisol levels in chimpanzees.Papio cynocephalus and anubis) varied considerably within and between years based on seasonal dynamics and diet variation, highlighting the susceptibility of phenotypic variance, and thus heritability estimates, to shared environmental factors70.In our study, while much of the variation in urinary cortisol levels was attributable to long-term group effects, short-term group-level (“group-year” random effect) and individual-level (“ID-year” random effect) factors had a strong influence on cortisol phenotypes. These short-term effects illustrate the flexibility of this physiological phenotype in wild animals but also raise an interesting phenomenon of group-level, shared experience of temporal variation in cortisol levels in chimpanzees. In a similar recent study, heritability estimates for microbiome traits in a population of wild baboons . However, we cannot exclude the possibility that the long-term community differences we observe in our study are in part driven by hangovers from extreme events, such as periods of high mortality from disease. Social bonds are key for cooperation in chimpanzees72 and losing keystone individuals within cooperation networks could lead to long-term reduced within-group cohesion73, in turn affecting cortisol levels across the entire group74. Instability could also arise if there are frequent switches in positions in dominance hierarchies and male rank instability has been shown to impact urinary cortisol concentrations in male Taï chimpanzees57. In most cases periods of rank instability tend to be short, lasting weeks or months rather than years, and would not necessarily explain the long-term group differences in cortisol levels in our study. Instead, social connectivity and social dynamics beyond rank may influence differences in group-level stress. We found a negative correlation between community size and cortisol levels . Therefore, we can tentatively discount this factor as the primary driver explaining between-group differences in cortisol levels. Chimpanzees are a highly territorial species78, and the incorporation of more fine-scale behavioral data regarding competitive factors, e.g. rates of incursion from neighboring communities75, will also be beneficial in future studies on between-group variation in cortisol levels.Regarding food availability and associated competition, the Taï communities are adjacent to one another, with relatively little between-group variation in the availability of food  of group-level stressors in our dataset is an interesting and important avenue of future research. As average cortisol levels are predictive of survival80. Therefore, adult chimpanzees are socially bound to their communities, within which they exhibit complex within-group cooperation83, in part to facilitate effective out-group directed aggression and in-group defense78. Although human cooperative tendencies can extend beyond an individual’s immediate community, cooperation remains partially constrained by in-group favoritism, which can lead to substantial between-group competition84. In some recent human studies, community-level differences in stress exposure have been related to community-level variation in social inequality and culture86. These community-level discrepancies in the experience of social or physiological stressors were further illuminated by the covid-19 pandemic87. Our results suggest that community-level approaches to reducing excessive exposure to stressors could be more effective than approaches targeting single individuals, both in our closest living ancestors, and possibly in humans and other long-lived species.Our results suggest a diminished role for heritable factors in shaping cortisol phenotypes for long-lived species. Remarkably, genetic factors had an even lesser role than the small but detectable effect of the maternal environment. In this species, males are the natal sex, while females typically disperse only once in their lifetime during adolescence28 found a limited role for parental care in shaping the strength of parental effects on trait variation. However, as far as we are aware, few species included in the study demonstrate the prolonged mother-offspring association observed in chimpanzees. We hope that our results will encourage studies in other animals with protracted developmental phases or maternal associations to compare and contrast the relative influence of mothers and genetic inheritance. As with the macroenvironmental socioecological effects identified, determining the specific mechanism leading to the observed maternal effect merits further study, with factors such as protracted maternal care, epigenetic processes, or shared experience of stressors being possible explanations89. In wild chimpanzees, although maternal loss impacts later life reproductive success37, there is no evidence that this is the result of long-term HPA axis activity alteration as effects on circadian cortisol patterns following maternal loss do not endure into adulthood30. This time-limited nature of alteration of the HPA axis activity suggests that effects of adversity, and potentially maternal effects, are not explained by epigenetic mechanisms.Given the strong contribution of individual- and group-level temporal effects on variation in cortisol levels, it is notable that we were able to identify such a clear maternal effect on individual differences in our study, a key aim of our study. Although absence of evidence is not evidence of absence, the lack of a clear genetic effect in our results at least indicates a qualitatively stronger influence of maternal identity in shaping cortisol phenotypes in our study population. In a recent meta-analysis, Moore et al.90, therefore, maternal social phenotype is the key determinant of the social environment of immature offspring. Previous research suggests maternal dominance status influences fecal GC levels in male, but not female immature chimpanzees91, therefore, status alone is unlikely to explain the full extent of the maternal effect identified in our study , or experimental animal studies employing less complex model structures (e.g. refs. 26). The pedigrees used in these studies vary considerably: human twin studies often sample between 100–200 twin pairs and their parents15, while certain animal studies can have over 1000 individuals within their pedigree (e.g.24). Despite drawing upon one of the largest datasets of its kind in a long-lived mammal , our pedigree is comparatively small, although not uniquely so among animal studies (e.g. ref. 23). Nevertheless, while the shallow nature of our chimpanzee pedigree can possibly explain the uncertainty of our estimates, it is unlikely to explain the reduced heritable contributions identified in our study. In our model, we included numerous socioecological predictors of cortisol levels in chimpanzees68 as well as a random effect structure that could capture the relative contributions of shared community, genetic, and maternal effects. Our Bayesian approach, which allows us to capture uncertainty in our heritability estimates, and our permutation analyses, which suggests our results are not expected simply because of the structure of our pedigree, means that the qualitative and relative contributions of heritable components in our study remain illuminating. Thus, while in general, it is difficult to compare proportions of variance between different studies when model structures and complexities vary substantially96, our study points to the fact that analyzing large datasets, encompassing different and diverse groups, could be key in revealing the relevance of different ecological conditions, shared community dynamics, and idiosyncratic group features in determining phenotypic traits. Moreover, based on results in other species, we do not contend that genetics have no influence on variation in cortisol in chimpanzees, only that any genetic effect is challenging to discern due to the clearer role of maternal and group-level socioecological effects. Therefore, previous research, due to methodological limitations in effectively partitioning environmental and maternal effects, may have overestimated the importance of genetics in the formation of cortisol phenotypes in long-lived species.In our study we find that the contributions of heritable, and particularly genetic, factors to cortisol phenotypes were low as compared to values reported in other analyses, such as human twin studies in which maternal and environmental effects cannot typically be measured . Although urinary cortisol concentrations derived from each of these methods are usually correlated , the ranges of values will be different, which may affect the degree of variation in quantified circadian slopes. Third, our models were inherently more complex in structure (e.g. the number of levels for each random effect and slope), which can make it difficult to compare repeatability between different studies96. We expect that our approach, necessary due to the question we are addressing and the size of the dataset, may not be optimal to evaluate the subtlety of individual differences in circadian slopes. Nevertheless, despite this discrepancy with Sonnweber et al.49, across both studies, we find clear evidence of consistent individual differences in average cortisol levels, regardless of differences in sample sizes, model complexity, and correction factors used in calculating cortisol concentrations.In our study, we were able to show that average cortisol levels given circadian effects are repeatable across demographics, including adult females in various reproductive states and in immature individuals. However, unlike Sonnweber et al.22. Similar work on other ape populations, including humans, is vital to fully understand the relative contributions of genetics and non-genetic environmental effects on cortisol regulation in this taxon. Indeed, recent studies show that community-level differences in stress exposure are prevalent in human societies86. Our results show that experiences of differing group-specific stress levels may be a common aspect of our evolutionary histories. Overall, determining the precise mechanisms of both maternal and non-maternal community-level influences clearly merits further investigation and will contribute to our understanding of the role of society, parents, and developmental plasticity in shaping physiology in long-lived, social species.To conclude, in our study, the macroenvironment of shared community effects and the microenvironment of maternal effects are the major influences on cortisol variation throughout the lifespan in chimpanzees. The lack of a clear genetic influence on cortisol regulation in chimpanzees was surprising, given the weight of evidence in human twin studies, and suggests caution in interpreting human studies when non-genetic factors such as non-genetic maternal effects or community effects cannot be factored out97;) and in Budongo Conservation Field Station, Uganda , data were collected from two communities of eastern chimpanzees (Sonso and Waibira99).We used long-term behavioral, demographic, and physiological data collected between 2000 and 2018 from two field sites of two subspecies of chimpanzee. In Taï National Park , Côte d’Ivoire, data were collected from three communities of western chimpanzees  and regular urine sample collection (see below) commenced in 2000 . In Budongo, regular observations of the chimpanzees commenced in 1994  and regular urine sample collection commenced in 2005 .In both Taï and Budongo, data on the chimpanzees are systematically collected by a combination of locally-employed field assistants and visiting researchers. Longitudinal data includes daily counts of group compositions, as well as recording of behavioral and social interactions using a combination of focal observations and ad-libitum samplingWe collected urine from identifiable individuals using a plastic pipette to transfer urine from the ground or vegetation into either a 2 ml or 5 ml cryovials. Cryovials were stored in liquid nitrogen once back in camp, typically within 12 h of collection. Frozen samples were transported packed in dry ice to the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, where they were stored at ≤20 °C in freezers.63 and MassLynx software101. We used prednisolone , or testosterone d4  as the internal standards. Samples analyzed using the “new method” tended to have higher urinary cortisol concentrations than those of the “old method”, therefore, we included LCMS methodology as a fixed effect in our statistical analyses (see below). Intra- and inter-batch coefficients of variation for quality controls were 8.29% and 13.59%, respectively.We quantified urinary cortisol levels for each sample using LCMS64:For each sample, we measured specific gravity (SG) using a refractometer . SG values were used to correct cortisol measurements for variation in water content in the urine using the formula outlined by Miller et al.The population means were derived from the samples included in this analysis. The SG population mean was 1.02 for Taï and 1.02 for Budongo.102. Dried samples were transported in silica to the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany.Fecal samples were collected from identifiable individuals. The samples were collected using plastic bags and then either directly stored in ethanol, dried on silica gel, or using a two-step ethanol-silica methodn = 259 for Taï; n = 169 for Budongo) has been conducted since 1999, with an average of 83% complete genotypes at 19 loci. Approximately 100 mg of each sample was extracted using either the QIAamp DNA stool (Qiagen) or the GeneMATRIX Stool DNA Purification (Roboklon) kits. We genotyped DNA extracts using a two-step amplification method including 19 microsatellite loci as detailed previously65. Using CERVUS 3.0 software103, we compared the resultant genotypes using the ‘identity analysis’ function to confirm individual identities and the ‘parentage analysis’ function to confirm maternities and assign paternities, using confidence assessments of 80 and 95%. In the case of fathers, each of the paternity assignments received a high likelihood, and other potential sires  were excluded by two or more mismatches. In total, were able to reliably assign fathers to 43% of individuals represented in the dataset.Using these samples, microsatellite genotyping of DNA of 428 individuals : pregnant (during the 240 days preceding the birth of any offspring), lactating (the 1095 days [based on average resumption of cycling in the population] subsequent to the birth of any offspring) and cycling . We included all adult female samples where we were able to assign reproductive state to the female at the time of sampling. Furthermore, following related studies59, we excluded samples from pregnant females because cortisol levels tend to increase during pregnancy. In fact, interactions can occur between maternal and fetal HPA axes making it difficult to accurately determine maternal cortisol levels in isolation105.In female primates, including chimpanzees, cortisol levels vary with reproductive state30. Therefore, if immature individuals lost their mother prior to the age of 12 years old , we excluded any sample collected from them following maternal loss during immaturity. However, as there is no evidence of long-term impacts of maternal loss in mature chimpanzees30, all mature individuals were included regardless of maternal loss during immaturity. Furthermore, injury and sickness can elevate cortisol levels and affect circadian cortisol patterns in chimpanzees71. At both Taï and Budongo, researchers systematically report any signs of illness of injury in the chimpanzees to onsite veterinarians. These reports are then validated and recorded by the veterinarians. Using these records, we excluded samples from individuals that displayed symptoms of sickness or injury on the day of sampling.In immature chimpanzees (<12 years old), maternal separation elevates cortisol secretion and has short-term effects on cortisol circadian patterns69. However, for one group in our study (Waibira), we had insufficient data to calculate ranks for the females, and in all groups, immature individuals are not traditionally considered part of the dominance hierarchy, and infrequently indicate submission to other immatures. Consequently, we were limited in the number of individuals we could confidently assign a dominance rank to, which would limit our sample size and pedigree depth for the heritability analyses. Therefore, in our main reported models, we did not include dominance rank as a fixed effect. Nevertheless, given the established importance of dominance rank in relation to stress in primates, we conducted a supplementary analysis. For mature adult males and females , dominance ranks were calculated using pant grunt vocalizations, a unidirectional call given from subordinate individuals106. We used a likelihood-based adaptation of the Elo rating approach to calculate ranks108; we assigned continuous Elo ranks to subjects for each day of sampling; each score was standardized between 0 (lowest rank) and 1 (highest rank) within each group. To be able to include immature individuals in our supplementary analysis, we converted ranks among adults into categorical variables: males and females with Elo rating equal to or greater than the mean on each day of observation were considered “high-ranking males” and “high-ranking females” respectively, those with ratings below the mean were considered “low-ranking males” and “low-ranking females” respectively. We then categorized immature individuals as either “offspring of high-ranking females” or “offspring of low-ranking females” based on the rank category of their mother on the day of observation. We took this conservative approach, as opposed to creating a linear hierarchy within immatures, as we have insufficient data within immatures to establish the hierarchy and a lack of evidence that offspring inherit rank in a linear fashion; indeed, anecdotal evidence suggests dominance among immatures is more likely to be driven by age than mothers. This new dataset included on 5,691 samples and 141 individuals; results from this separate heritability analysis using this dataset are reported in Table Lastly, in male and female chimpanzees respectively, there tends to be a positive and negative correlation between dominance rank and GC levels30.To ensure that we were able to characterize circadian cortisol patterns for each individual, we only included individuals with a minimum of 3 urine samples per year, collected during both morning and afternoon hours, such that the earliest and latest samples were separated by at least 6 h. The reason for this criterion was to be able to model a meaningful quadratic circadian slope for each individual within a given year of sampling90. In addition to the demographic categorization  and age of each individual on the day of sampling, we included in the analysis a number of control variables known to influence cortisol levels. Both group size and mating competition60 can affect GC levels in primates, therefore, we calculated both the number of adults  and the male-to-female sex ratio  at the time of sampling for each sample. Lastly, as seasonal variation in rainfall, temperature, humidity and food availability can influence cortisol levels in chimpanzees62, we accounted for this circannual variation by converting the Julian date of sampling into a circular variable and including its sine and cosine in our models109.To accurately model circadian patterns of cortisol for all individuals (our measure of cortisol reaction norm), we included interactions between the linear and quadratic time variables and all other fixed effects. We used 12 years of age to distinguish between adult (aged ≥12 years) and immature individuals (aged <12 years), as it is the age at which individuals socialize and forage predominantly independently from their mothers110 and the RStudio interface111. Prior to testing our models, we applied the vif function of the ‘car’ R package112 to linear model versions of our mixed models (i.e. lacking random effects) to test for any collinearity issues via examination of variance inflation factors (VIF). There were issues with collinearity if either “site” or “group” were included in the models as both variables were either collinear with each other or with “group size”. Therefore, we retained just “group size”, with all remaining VIFs <2.90. “Group” and “group-year” were also included as random effects to account for group-level confounds. To further test potential temporal batch effects, we ran additional models using a year-month random effect . The results ) and for the random effects ), with uniform (LKJ(1)) priors for covariance matrices of the random slopes. For all models, we specified four chains of 4000 iterations, half of which were devoted to the warm-up. Sampling diagnostics (Rhat <1.1) and trace plots confirmed chain convergence for all models. Effective sample sizes confirmed no issues with autocorrelation of sampling for all models. We further validated models with posterior predictive checks using the pp_check function of ‘brms’  to time of day, Vlinear, and the variance in the quadratic response (slope) to time of day, Vquadratic. These were computed as total variance excluding that explained by predictors related to technical aspects of the data (i.e. the project random effect).We calculated the repeatability and heritability of reaction norms following the same approach as referencesVtotal) was calculated as the sum of variance explained by individual identity across years  and within years, i.e. the ID-year variable (VID-year), as well as variance explained by group identity across years (Vgroup), and within years, i.e. the group-year variable (Vgroup-year).For the repeatability analysis, total within-group variance in urinary cortisol concentrations  was calculated as:Vindividual/VtotalShort-term trait repeatability  was calculated as:VID-year + Vindividual/VtotalReaction norm repeatability of average cortisol levels  was calculated as:Vindividual/Vlinear,individual) and ID-year  random effects:Reaction norm repeatability of linear cortisol responses to time of day  was calculated using the variance of the random slope estimates for the linear term for time of day within the individual identity Similarly, the reaction norm of quadratic cortisol responses was calculated as:Vquadratic,individual/Vintercept, Vlinear, and Vquadratic, we partitioned the variance within groups (Vwithin) and between groups (Vgroup).For the heritability analysis and for each of Hence, the proportion of variance explained by the shared community effects is:Variance in average cortisol levels explained by shared community effects =Vgroup, intercept/Vgroup, linear/Variance in linear cortisol responses to time of day explained by shared community effects = Vgroup, quadratic/Variance in quadratic cortisol responses to time of day explained by shared community effects = Full heritability model:The within-group variance is defined, for each component of the reaction norm, as the sum of the variance explained by all biological predictors except group identity. Hence, for the Vwithin,intercept) = Vgenetic, intercept + Vmother, intercept + Vgroup-year, intercept + Vindividual, intercept + VID-year, interceptWithin-group variance in average cortisol levels  = Vgenetic, linear + Vmother, linear + Vgroup-year, linear + Vindividual, linear + VID-year, linearWithin-group variance in linear cortisol responses to time of day  = Vgenetic, quadratic + Vmother, quadratic + Vgroup-year, quadratic + Vindividual, quadratic + VID-year, quadraticWithin-group variance in linear cortisol responses to time of day www.eva.mpg.de/primat/ethical-guidelines.html) for the ethical treatment of non-human primates.All methods were non-invasive and were approved by the Ministries of Research and Environment of Côte d’Ivoire, and Office Ivoirien des Parcs et Réserves. All aspects of the study comply with the ethics policy of both the Max Planck Society and the Department of Primatology of the Max Planck Institute for Evolutionary Anthropology, Germany (Further information on research design is available in the Peer Review FileSupplementary MaterialReporting Summary


REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a  For example, prior work performed GWAS on each recorded point of electrocardiograms to identify its genetic architecture . There aMachine learning-based (ML-based) phenotyping uses HDCD as input to a supervised machine learning model to predict trait labels, and then performs GWAS using the model predictions as the target phenotype , 12, 6. 1), FEV1/FVC, peak expiratory flow (PEF) and forced mid-expiratory flow (FEF25–75%) , all volume curves whose values are not in , and all flow curves in which the proportion of nonzero values are less than 20%. Finally, we generated flow-volume curves from volume-time and flow-time curves by interpolating 1000 evenly spaced volume values between 0 and 6.58 liters (the maximum observed volume in the dataset).Spirograms from UK Biobank were sourced from the data field 3066, which contains the volume in milliliters of exhalation at 10 millisecond intervals (volume-time curve) and were preprocessed closely following the procedures in Cosentino et al. [We then subdivide all European ancestry individuals processed this way into a 80% training set and a 20% validation set. After additionally removing related individuals, there are 259,692 individuals in the training set and 65,266 individuals in the validation set .Asthma and COPD status were determined by medical records using self report, ICD9, and ICD10 codes as defined in Cosentino et al. .4.2Photoplethysmograms (PPG) from UK Biobank were sourced from the data field 4205, which contains the arterial stiffness pressure curve. Each waveform is actually a single pulse with 100 points. Then we computed the minimum, maximum, mean, and median distribution value of PPG. We keep the PPG when all four statistics fall in 0.1 and 99.9 percentiles of the related statistics values of all PPGs. We then subdivide all European ancestry individuals processed this way into a 80% training set and a 20% validation set. After additionally removing related individuals, there are 112,730 individuals in the training set and 28,545 individuals in the validation set .Hypertension status was determined by medical records using self report, ICD9 codes (401.* and 405.*), and ICD10 codes (I10 and I15.*). Systolic blood pressure (SBP) was determined by automated reading and Data-Field 4080 was used in UK Biobank.4.3To generate SPINCs, we encode the flow-time and volume-time curves. In our VAE, we use one-dimensional convolutional layers to utilize the temporal context of this time series, encoding the two curves in two channels. In the encoder, we first apply three 1D convolutional layers, each followed by max pooling, and use three fully-connected layers to generate the mean and variance of the bottleneck layer. We use 5 latent dimensions, identical to the number of EDFs, and each latent coordinate is sampled from the Gaussian distribution with the learned means and variances. The decoder architecture is a mirror image of the encoder. We start with three fully-connected layers followed by transpose convolutions layers, each prepended by an upsampling layer. See “For RSPINCs, we encode the flow-volume curve alone, and we apply the same sequences of convolutional and fully-connected layers as we did for SPINCs, while using only 2 latent dimensions in this case. Importantly, we use a novel VAE architecture to concatenate the 5 EDFs directly to the sampled output of the bottleneck layer (the layer right before the decoder) to learn only the residual signals not represented by the EDFs . As a reFor PLENCs, we encode the PPG curves. In our VAE, we use one-dimensional convolutional layers to utilize the temporal context of this time series. In the encoder, similar to SPINCs, we first apply three 1D convolutional layers, each followed by max pooling, and use three fully-connected layers to generate the mean and variance of the bottleneck layer. We use 5 latent dimensions, identical to the number of EDFs, and each latent coordinate is sampled from the Gaussian distribution with the learned means and variances. The decoder architecture is a mirror image of the encoder. We start with three fully-connected layers followed by transpose convolutions layers, each prepended by an upsampling layer. See “PLENCs model architecture” in All models are trained using the standard VAE loss function consisting of the reconstruction loss and the  Kullback–Leibler (KL) divergence loss. For RSPINCs the KL divergence loss is only applied to the learned encodings, not to the injected EDFs. For optimization, the Adam optimizer  is used After training SPINCs, RSPINCs, and PLENCs models, we use the encoders of the trained models to generate the encodings for each individuals, using the mean value of the learned Gaussian distribution of the encodings.All models were implemented in TensorFlow V2  and XMan4.4P-value with two-sided alternative hypothesis.To residualize EDFs and/or covariates from (R)SPINCs and PLENCs, we used ordinary least squares linear regression. To compute the correlation of the EDFs-and-covariates-residualized (R)SPINCs and PLENCs with the tabular fields in UK Biobank, we first preprocessed the tabular fields to remove special codes, normalize, impute and aggregate the values, and finally transformed the categorical fields into one-hot encodings. For each individual correlation analysis between a feature and one of the (R)SPINCs and PLENCs, we computed the Pearson correlation and the 4.5n=65,266) and PPG  validation sets using the time from first assessment (field 53) to death from any cause (field 40000). Subjects who were not known to have died were right-censored at the date of UKB data ingestion . We quantified the association between overall survival and each SPINC, RSPINC, PLENC, and EDF per standard deviation using the hazard ratio, which was estimated from a Cox proportional hazards model adjusting for age and sex. The proportional hazards assumption, with respect to each SPINC, RSPINC, PLENC, and EDF, was assessed using the Schoenfeld residual test. After stratifying patients into quartiles using each SPINC, RSPINC, PLENC, or EDF coordinate, the overall survival curves in We performed analysis of overall survival for European individuals in the spirometry  calculation contained 10,000 unrelated European samples from the UKB. Significant “loci” were created based on the span of reference panel SNPs in LD (R2 ≥ 0.1) with the hits. Loci separated by fewer than 250 kb were subsequently merged.All GWAS were restricted to European ancestry individuals to minimize confounding. For quality control we kept variants with minor allele frequency ≥ 0.001, imputation INFO score ≥ 0.8, missing call fraction ≤ 0.05, and Hardy-Weinberg equilibrium d S-LDSC  to estimpredBetasFile option of BOLT-LMM, which generates PRS coefficients using a Bayesian linear mixed model. While GWAS was performed on individuals with valid spirometry measurements, we evaluated the performance of the PRS in a separate set of individuals not used for GWAS.While performing GWAS, PRSs for all traits were computed using the –v1.0.2-associations_e106_r2022-07-09) [To determine the known lung function loci from previous literature, we extracted all significant loci from Shrine et al.  and sear2-07-09) . We used4.71.93.3beta) and we set –cojo-cond to the set of known loci. We provided 10,000 unrelated European samples randomly chosen from UKB as the reference samples, which is the same reference used to perform LD clumping to define hits, as part of GCTA-COJO input parameters.We applied conditional and joint analysis (COJO) on set of previously known loci using GCTA  software  reference panel using Michigan Imputation Server [COPDGene is a study of 10,300 current and former smokers with and without COPD, self-reported non-Hispanic White and African-American, without known lung diseases other than COPD and asthma (dbGaP accession phs000179.v6.p2). Additional study details, the study protocol and details of genotyping have been previously published , 67, andn Server , resultiThe European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) is a general population-based cohort study of men and women aged 40–79 years living in Norfolk, UK and recruited from general practices between 1993 to 1997. EPIC-Norfolk cohort participants were linked annually to nationally held hospital records and death certificates from 1999 to 2019 using UK National Health Service numbers. COPD was defined as any hospital admission or cause of death coded 490–492, 494–496 (ICD-9) or J40–J44, J47 (ICD-10). Asthma was similarly defined using codes 493 (ICD-9) or J45, J46 (ICD-10). Hypertension was defined using hospital records and death certificates for ICD codes 401.*, 405.* (ICD-9) and I10, I15.* (ICD-10). The systolic blood pressure was determined using the continuous systolic blood pressure from EPIC-Norfolk health examination at baseline, which is the time-point with the highest number of individuals. In a small set of participants who do not have a baseline blood pressure measurement, we used blood pressure measured at the earliest subsequent health examination. Blood pressure was measured at two time points during the examination, with the mean used for analysis.We utilize five consent groups that does not require IRB approval: General Research Use (GRU), Health/Medical/Biomedical-Genetic Studies, (HBM-GSO), Health/Medical/Biomedical (HMB), Health/Medical/Biomedical (MDS) HMB-MDS, and Health/Medical/Biomedical  (HMB-PUB-GSO) (dbGaP accession phs001584.v2.p2). In eMERGE III, we have access to 1,038 asthma cases and 7,250 controls for European ancestry while in the case of African ancestry we have access to 649 asthma cases and 1,367 controls. We used the 39,131,578 variants that are imputed to the HRC reference provided by dbGaP . Asthma P-value < 1 × 10−10 among cases and < 1 × 10−6 in controls were excluded as reported previously [R2 < 0.30 and MAF< 1% were excluded. PLINK [The Indiana Biobank is a state-wide collaboration that provides centralized processing and storage of specimens that are linked to participants’ electronic medical information via Regenstrief Institute at Indiana University. COPD was diagnosed by using ICD9: 491, 492, and 496, and ICD10: J41, J42, J43, and J44. Asthma was diagnosed by using ICD9: 493, and ICD10: J45 and J46. Cases were defined as having at least one in-patient diagnosis or two out-patient diagnoses. Those participants not having any diagnosis were defined as controls. Thus, we have 1,445 COPD cases and 3,808 controls while we have 1,171 asthma cases and 4,083 controls. Among 5,253 individuals for COPD evaluation, 3797 were of European ancestry, 1,371 of African ancestry, and 85 Hispanic ancestry. Among 5,254 individuals for asthma evaluation, 3805 of European ancestry, 1,362 of African ancestry, and 87 of Hispanic ancestry. Indiana Biobank samples used in this study were genotyped using Illumina Infinium Global Screening Array  by Regeneron . SNPs with missing rate > 5%, MAF ≤ 1%, HWE eviously . Imputedd. PLINK , 71 was 4.9We ran GREAT v4.0.4  on the h4.10We applied LCV  (URLs) o4.11–clump command with an index variant significance threshold of 5 × 10−8 and LD threshold of 0.1, with LD computed from a random subset of 10,000 European individuals in UK Biobank.To compute PheWAS, we downloaded GWAS summary statistics for 7,221 phenotypes from the Pan-UKB consortium 20200615 release (URLs). After restricting to phenotypes that contained European statistics and did not persistently fail in LD clumping, we were left with 7,145 pruning+thresholding PRSs generated by PLINK (URLs) using the P-values with two-sided alternative hypothesis.SPINCs, RSPINCs, PLENCs pruning+thresholding PRSs were computed analogously to the Pan-UKB PRSs. We computed the Pearson correlations between the PRSs derived from latent dimensions with the PRSs derived from Pan-UKB phenotypes and the Supplement 1Supplement 2