Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes.N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality.Leveraging genetic and health registry data from the UK Biobank and FinnGen . Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes.Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences. Hormones, such as testosterone, travel around the body communicating between the different parts. Testosterone is present at higher levels in men, but also present in women. Variable testosterone levels explain some differences in human traits and disease prevalence. Here, we study how adult testosterone levels relate to health and disease. Genetic, i.e. inherited, differences in testosterone levels contribute to many traits specific to men or women, such as women’s reproductive health, hormonal cancers, and hair growth typical in males. However, testosterone levels do not appear as a major cause of most traits studied, including psychiatric diseases and metabolic health. Normal variation in baseline testosterone levels thus seems to have a relatively minor impact on health and disease. Leinonen et al. investigate correlations between testosterone levels and disease using genetic and health registry data from the UK Biobank and FinnGen. There is a lack of evidence for normal variation in testosterone levels having a causal contribution to most non-sex-specific traits. Epidemiological studies and randomized clinical trials for T replacement therapy have observed associations between serum T levels and various traits ranging from type 2 diabetes (T2D) and cardiovascular disease to body composition and behavior11. Yet, these studies have yielded partly mixed results, and, in many instances, the proposed relationships between T, complex traits and disease remain elusive11.Testosterone (T) is the male sex hormone responsible for regulation of development of primary and secondary male sexual characteristics. Individual variation in T levels has been suggested to shape human physiology broadly, including effects on disease risk in both men and women12. This difference largely results from the testicular T production in males that far exceeds the amount of T produced in the ovaries and the adrenal gland in females, and is known to directly contribute to variation in, for instance, body composition between the sexes12.Besides the disease links, T is a known driver for sex differences. After puberty, men and women differ extensively with respect to their average T levels, with males showing roughly 7–15-fold higher serum total T concentrations16. Free T is considered to represent the most potent form of T in terms of biological activity, and although extensively debated may have different clinical significance than total T16. Most of the remaining T in circulation is tightly bound by sex hormone-binding globulin (SHBG), and the bulk of the rest remains attached to carrier proteins like serum albumin  of this total T exists as free Tmin Fig. 4,13,14,118. However, twin studies indicate that heritability of serum T is relatively high in both sexes, being up to 65% in males20. Until recently, few genetic variants were shown to associate with circulating T levels and related traits in the general population22. Yet, through the emergence of genetic and biomarker data from large biobanks, currently more than a hundred, mostly autosomal, loci for serum total T, SHBG and free T have been identified, with evidence for sex-specific genetic effects25.Complex physiological processes regulate circulating T levels, allowing T levels to fluctuate based on internal and external stimuli on a daily basis28. Here, we extend these investigations combining data from the UK Biobank  and FinnGen . Leveraging the extensive healthcare registry data of FinnGen, sex-specific polygenic scores (PGS), and public data from genome-wide association studies (GWAS), we studied the role of T levels in both sexes across traits ranging from metabolic conditions and sex-specific reproductive disorders to neurological and behavioral endpoints.Recent efforts have used genetics to address the potential causal contribution of adult T to selected complex traits in both sexes, including T2D, body composition and hormonal cancers, or have examined the effects of free T more broadly in males based on the UK Biobank dataWe illustrate that normal variation in genetically set T levels has only subtle effects to most human traits. For example, the association between T and many metabolic phenotypes may instead largely stem from shared etiology, challenging some previous findings. Yet we stress the unique genetic architecture of serum T levels, potentially reflecting sexually antagonistic effects of T on fitness, and emphasize the causal role of the hormone also for women’s health.29. Approximately 74% of the female participants in the cohort have reportedly undergone menopause. At recruitment, participants provided electronic signed consent. Ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee (11/NW/0382). All experiments were performed in accordance to relevant guidelines and regulations including the Declaration of Helsinki ethical principles for medical research. This study was run under UK Biobank application number 22627.The genetic association analysis was based on data from the UK Biobank, a population-based biobank consisting of 502,637 subjects (aged 37–73 years)https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf. The document contains also details about the extensive QC procedures for all biochemical measurements in the UK Biobank. Analytical range for the immunoassays of total testosterone and SHBG were 0.35 to 55.52 and 0.33 to (226–242) nmol/L, respectively. Assay consistency was monitored by using internal QC samples between batches, and external quality assurance schemes against the ISO 17025:2005 standard. Notably, the reported testosterone and free T levels in the UK biobank are on average lower than for many other cohorts, for example YFS  allows for their inclusion. To ensure that the UK Biobank measurements capture baseline heritable variation in adult T levels, we assessed correlation of testosterone measurements between two timepoints available for a subset of a UK Biobank population , with the original samples collected between 2006 and 2010, and additional samples taken in 2012–2013. T levels from these two time points showed a high correlation . Calculated free T was derived using the Vermeulen equation using directly measured albumin values for each participant31.For the GWASs, we used biochemically measured testosterone and SHBG, and calculated FAI and free T. For clarity, given the close relationship between free T and FAI, in the results section we concentrate on describing the results for free T, whereas the results for FAI are available in the supplementary data. For calculation of FAI, we used the formula All four traits were separated by sex and log transformed. Subjects with residuals values of +/−5 SD from the mean were excluded from the analyses, serving as a further QC step to exclude outliers potentially reflecting medical conditions or drug use affecting androgen levels. Inverse normalized values of the remaining residuals were used as phenotype values for the GWAS analyses. After the QC steps, our study included altogether 177,499 men and 205,141 women.33. Imputed SNPs were restricted to variants with MAF ≥ 0.1 % and imputation quality ≥0.729. We included both autosomal and X-chromosomal variants into the analysis, excluding pseudoautosomal regions (PARs). X-chromosomal effects in males were based on  allele dosage coding. 1000 Genomes European data was used as reference LD scores for calibrating the BOLT-LMM statistic. First 10 Principal Components were used as quantitative covariates in the runs. A linear regression model was fit for each trait with BMI and age as covariates, as well as menopause status for females. Genetic correlation analyses, heritability estimates and number of loci found implied the results remained consistent with different covariate configurations , e.g., assay center, dilution factors, blood draw time, and socioeconomic status indicators, or excluding related individuals from the analysis all showed negligible effects on the genetic findings p-value  from the GWAS summary statistics and removing all SNPs 500 kb on each side of it from the next round. The chromosomal positions of these 1 mb windows were stored, and overlapping windows were merged into the final list of loci. The SNP with the lowest p value in each of these windows was selected as the lead SNP.Independent lead SNPs were selected for each chromosome by recursively taking the SNP with the lowest N = 408,186)    for the loci being shared, we used gwas-pw37.Tissue and gene set enrichment analyses were carried out with SNP2GENE and GENE2FUNC implemented in FUMA using default settingsThe Cardiovascular Risk in Young Finns Study (YFS) is a longitudinal follow-up of 3,596 subjects at baseline. The baseline survey was conducted in 1980 and subsequent follow-ups involving the whole sample were held in 1983, 1986, 2001, 2007, 2011, and 2017. The YFS study has been approved by the ethical committee of the Hospital District of Southwest Finland, all study subjects gave an informed consent, and the study has been conducted according to the principles of the Declaration of Helsinki. For testosterone and SHBG, we used data on 2001 follow-up (Subjects aged 24–39 years). A venous blood sample was drawn from antecubital vein after 12-h overnight fast. Serum was aliquoted and stored at −70 degrees Celsius until analysis. In males, total testosterone quantification was performed in 2009 with competitive radioimmunoassay  and Bio-Rad Lyphocheck control serums 1, 2, and 3 were used in quality control. Before quantification, serum aliquots had been melted three times. Total testosterone was quantified first and aliquots were re-frozen before SHBG quantification. In females, total testosterone quantification was performed in 2011. SHBG quantification for males was done in 2009 and for females in 2011 with Spectria SHBG IRMA kit . Free testosterone was estimated using Vermeulen’s formula. As albumin concentration was not available, we used fixed albumin concentration of 43 g/l.p < 1 × 10^−6) and minor allele frequency below 1% were excluded. Related samples were excluded (n = 51) with pi-hat cut-off of 0.2. Total of 2442 individuals and 546,674 variants passed the quality control measures. The mean call rate across all included markers after the quality control was 0.9984. Next, imputation was performed using population-specific Sequencing Initiative Suomi (SISu) as reference panel. We examined the association of the UK Biobank GWAS lead SNPs with the corresponding T trait in YFS. If the annotated lead SNP was not available in YFS, we used LDstore2 (v2.0b)38 to calculate LD in a 100 kb window around the lead SNP in the UK Biobank imputed data and selected the closest SNP with R2 > 0.8 with the lead SNP as a proxy.In YFS, genotyping was performed at the Wellcome Trust Sanger Institute (UK) using customized Illumina Human Map 670k bead array. The custom content on 670k array replaced some poor performing probes on Human610 and added more CNV content. As quality control, we excluded individuals and probes with over 5% of missingness (--geno and --mind filters in Plink). Variants deviating from Hardy-Weinberg equilibrium . Only variants imputed with high confidence (imputation INFO > 0.8) were included in PGS calculation. Variants in chromosomes 1–22 and chrX were included. In males, allele dosage of 2 was used for X-chromosomal haploid variants. To evaluate the PGS prediction accuracy in the YFS, we calculated the R2 for each trait using using linear regression with z-score normalized PGS as predictor, age and 10 PCs as covariates and z-score normalized T trait as outcome.To construct PGSs we applied the LDpred40. The summary statistics for the 44 traits were downloaded directly from the source repositories and analyzed locally, for the original sources please see references in Supplementary Data SNP-based heritability for the studied T traits and genetic correlations between these and with 44 additional phenotypes were estimated using linkage disequilibrium score regression (LDSC), including autosomal variants only41. FinnGen is comprised of Finnish prospective epidemiological and disease-based cohorts and voluntary biobank samples collected by hospital biobanks. The genotypes have been linked to national hospital discharge (available from 1968), death (1969–), cancer (1953–) and medication reimbursement (1964–) registries as well as the registry on medication purchases (1995–). Although specific menopause status for the participants is not available, roughly 67% of the females in R5  are estimated to have undergone menopause at the end of the follow-up (age >50). The samples were genotyped with Illumina and Affymetrix arrays . The genotypes have been imputed with using the SISu v3 population-specific reference panel developed from high-quality data for 3775 high-coverage (25–30×) whole-genome sequencing in Finns. The detailed genotype imputation workflow can be found at 10.17504/protocols.io.xbgfijw. The dataset uses genome build 38 (hg38). A full list of FinnGen consortium members can be found from Supplementary Information.To assess if the PGS for studied traits associate with disease risk we utilized the FinnGen study (data freeze 5), consisting of 217,464 www.finngen.fi and risteys.finngen.fi. Cox proportional hazards models were used for estimating hazard ratios (HRs) and 95% CIs, with age as the time scale and 10 first principal components of ancestry and genotyping batch as covariates. The proportionality assumption for Cox models was assessed with Schoenfeld residuals and log-log plots. We performed power analyses on the Cox models using the powerEpiCont.default from powerSurvEpi package in R. We estimated that with the selected p-threshold our study is well-powered to reliably detect large effects (Hazard Ratio (HR) > 1.3) the PGSs have on rare endpoints such as hirsutism, and smaller effects (HR > 1.1) on the vast majority of the studied diseases  as for YFS, and calculated genome-wide PGSs for each individual with PLINK2 (v2.00a2.3LM). Variants in chromosomes 1–22 and chromosome X  were included  and we used genotype dosages to incorporate imputation uncertainty. In males, allele dosage of 2 was used for X-chromosomal haploid variants. We studied the PGS associations to 36 disease endpoints with potential links to androgens, representing six loosely defined disease categories. For details of the studied phenotypes see Supplementary Data Patients and control participants in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older research cohorts, collected prior the start of FinnGen (in August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen study protocol Nr HUS/990/2017.The FinnGen study is approved by Finnish Institute for Health and Welfare (THL), approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019, Digital and population data service agency VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3 the Social Insurance Institution (KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, and Statistics Finland TK-53-1041-17. The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 6 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017, and Terveystalo Biobank STB 2018001.46 biochemical traits such as T levels seem to less affected by such confounders. The second limitation  is harder to control when using a large number of SNPs as instrumental variables. Susceptibility to confounding by pleiotropy , overall presents a special issue for genetic studies.MR analyses treat genetic variants as instrumental variables and their reliability depends on two key assumptions: (1) alleles are randomly assigned, and (2) that alleles that influence exposure do not influence the outcome via any other means. The first assumption is partly controlled by using BOLT-LMM as our model in the primary GWAS analysis together with using PCs to control for population stratification. Although demographic phenomena such as assortative mating (mate choice based on similar characteristics in spouse) may affect effect estimates for measures such as educational attainment, based on within-sibship GWASLIN28B48, GCKR37 and TYK249). The cross-sex analyses further supported the notion that such pleiotropic variants  may partly underlie, e.g., T’s correlations with T2D and hypothyroidism.In this case, in line with the observed wide-spread genetic pleiotropy affecting most complex traits, we noted that the GWAS loci contained many genes associated with pleiotropic effects on human phenotypes , besides its recommended use as a sensitivity analysis for conventional MR54. For T traits, we additionally compared the causality results of these models to estimates from CAUSE, a MR method similarly designed to distinguish pleiotropy from true causality ), that remains sensitive for confounding by genetic correlation and pleiotropy53. To extend the basic MR Egger analysis and to tease out the potential effects of SHBG on causality estimates of total and free T, we used multivariable MR Egger56. LCV reports genetic causality proportion (GCP) as an estimate of causality, under a model where genetic correlation between two traits is mediated by a latent variable having a causal effect on each trait. GCP = 1 means trait 1 is fully correlated with the latent variable, and hence fully causal to trait 2. A high GCP value and a statistically significant effect support partial genetic causality between the traits, and suggest that interventions targeting trait 1 are likely to affect trait 2. The p value obtained in the analysis refers to the null hypothesis that the GCP = 0. A highly significant p value does not require a high GCP, but in the presence of lower GCP suggests only partial causality between the traits. Positive GCP value indicates causality of trait 1 to trait 2, whereas a negative value indicates support for causality of trait 2 to trait 1. LCV also estimates genetic correlation between the traits. Notably, LCV assumes a single latent variable mediating the effect between two phenotypes, and that the effect can be only unidirectional. Hence it may be confounded by bidirectional causal effects or by the presence of several latent variables. Given these limitations of the methods, despite the vast potential of MR in establishing causal relationships57, we however propose caution in interpreting these results: also in our case the MR models did not always agree on causality, common to recent MR studies assessing the function of T26.Therefore, given the vast polygenicity of the studied T traits, we chose latent causal variable (LCV)β = MR estimate, H = Hemoglobin in UK Biobank based on ref. 58. We applied the LCV, MR-Egger, CAUSE, multivariate MR-Egger and IWV models locally using R 4.0.2. The MR analyses were run using TwoSampleMR (v0.5.2)59 and MendelianRandomization (v0.5.0) R packages60. These causality analyses on the FinnGen traits included both autosomal and X-chromosomal SNPs. For all causality analyses X-chromosomal effects in males are presented for  allele dosage coding. For the traits from public GWAS included in genetic correlation analyses, in 16 out 44 instances we could perform two sample MR (phenotype data not including UK Biobank samples) and in 19/44 instances the studies included X-chromosomal data *β*SD_H_m)/((H_m-H_f)/SD_H_m), where FT = mean free Testosterone, m = males, f = females, SD = standard deviation, Further information on research design is available in the We utilized the rich biochemical and health information available in two population-scale genetic datasets and analysis methods building on GWAS discovery. In brief, we conducted sex-stratified GWAS for T, SHBG and free T based on immunoassay measurements from the UK Biobank, and constructed sex-specific polygenic scores (PGS) for these traits. The PGSs capture the genetic effects on T and SHBG levels, and therefore serve as a proxy for cumulative post-pubertal T exposure. Using an external dataset , we validated the performance of the PGSs. We then investigated the effects of the PGSs on a wide range of diseases across diverse clinical entities using the FinnGen study. Lastly, we evaluated causal relationships and genetic correlations between the studied T traits and complex traits, leveraging publicly available GWAS summary statistics Fig. .p < 5e-08) loci the autosomes and the X chromosome combined for all testosterone traits  in the UK Biobank GWAS  estimates ranging from 10% for total T in females to 28% for SHBG in males  = 0.88, p = 9.7e-197), whereas we observed a near-zero genetic correlation estimates for both serum T and free T between males and females , indicating sex-specific genetic determinants, as previously reported24. Co-localization analyses between male and female GWASs confirmed the widespread sex-specificity of the genetic loci for T . Correspondingly, SHBG was found causal for total T levels in males  = 0.80, p = 5.8e-05), whereas in females SHBG appeared to control the free T fraction .Reflecting the sex-specific genetic architecture, we observed strong genetic correlation between total T and SHBG only in males (39 to the sex-specific GWASs. We tested the predictive ability of the PGSs in the YFS where the phenotypic variance explained by a genome-wide PGS (R2) ranged between 1.1%  to 9.2% , indicating the PGSs predict T and SHBG levels in an independent cohort  endocrine and metabolic diagnoses, (2) sex-specific endpoints ), (3) cardiovascular and circulatory system, (4) nervous system disease, (5) behavioral and neurological diagnoses and (6) other endpoints like injury risk . In contrast, higher free T PGS in women increased risk for both . Given the shared association profiles with SHBG, we included SHBG as a covariate in complementary analyses to distinguish true T-driven effects. These suggested that associations to metabolic traits were not primarily attributable to androgen action (Supplementary Data p = 6.5e-05), persisting SHBG adjustment . We also detected suggestive associations to bone strength and injury risk in both sexes, but with the exception of SHBG and osteoporosis in females , none of these findings survived correction for multiple testing.Underscoring T’s and SHBG’s involvement in metabolism, larger PGS values for total T and SHBG were associated with reduced T2D risk and statin use in men  , with connections to PCOS, and endometrial cancer, respectively64. The association to hirsutism  appeared particularly pronounced, with the risk of the condition almost doubling with a 2 SD change in T. In FinnGen, there was only modest case and genetic overlap between PCOS, hirsutism and PMB cases, potentially reflecting underdiagnosis of PCOS in the dataset65. SHBG adjusted analyses further suggested these associations were androgen dependent (Supplementary Data p = 0.00502), seemed highly dependent on SHBG, showing generally favorable effects on reproductive traits, e.g., HR = 0.98, p = 0.00116 for irregular menstruation).In the sex-specific category, we replicated the known associations of T to PCOS and breast cancer risk in womenGS) Fig. . Of the p > 0.0014). However, the PGSs did show some nominal clinically-relevant associations: higher PGSs for male free T was linked with prostate cancer and lower osteoporosis incidence . Male total T associated with stroke , and female total T with cardiac death  and Parkinson’s . Both male free T and female total T PGS showed associations to lower anemia risk . For the sex-specific PGS data, in 23/252 instances one or both methods suggested evidence for a causal relationship (p < 0.0014) between a PGS and a disease  and LCV of free T to PCOS  . Here, adjusting for PCOS had negligible effects on MR results, suggesting these phenotypes are primarily dependent on direct androgen load, and not on metabolic disease . Notably, genetically predicted free T was linked with cancer risk also in the opposite sex .In contrast, the causality analyses strongly supported the role of T in the regulation of female reproductive health. For instance, MR Egger supported causality of total T (17) Fig. , indepenβ = −0.43, p = 0.0079). T and SHBG were linked with injury risk in both sexes . Finally, LCV suggested a causal relationship between higher SHBG and ADHD in both sexes, and higher free T and increased risk for conduct disorder but decreased risk for emotionally unstable personality in men   sex-specific associations remained similar (Z-test p > 0.05) in the cross-sex analyses, pointing to genetic pleiotropy rather than T action as the basis for many metabolic associations. For example, both female and male total T PGSs associated with hypothyroidism risk with a similar effect size in the opposite sex, and all PGS associations to T2D were replicated in the other sex  in the opposite sex for male total T PGS and stroke, female total T PGS and anemia, and male free T PGS, head injury risk and osteoporosis. , this associated with increased risk for stroke in women . This was the only endpoint for which we detected evidence for sexual antagonism. The antagonistic effect strengthened with SHBG adjustment . The results indicate the genetic effects on stroke risk may be partly sex-specific66, with potential interplay from sex hormones.The associations differed  or closely reflecting the studied disease phenotypes , mood swings) from FinnGen, adding anthropometric traits to the analyses   while lowering triglycerides and T2D risk in men . In contrast, higher free T fraction in women associated with negative metabolic effects, including higher WHR  and lower HDL cholesterol . Relatedly, strong correlations were observed between SHBG and metabolic traits in women, including protective associations to markers of liver damage , rg = −0.21, p = 1.7e-08) and gamma-glutamyl transferase (GGT), rg = −0.16, p = 0.0001).The genetic factors increasing serum total T and SHBG overall promoted a favorable metabolic profile in males. Despite correlating with increased BMI, total T and SHBG were positively correlated to adiponectin, high-density lipoprotein (HDL) and lower waist-to-hip ratio (WHR) , whereas in women these increased especially the risk of estrogen receptor (ER) positive breast cancer . Finally, consistent with the existing genetic, epidemiological and experimental data26, we found evidence for shared genetic background for T, hemoglobin levels and body fat in both sexes .We observed significant genetic correlations to hormonal cancers in both sexes, as expectedp < 0.0011) of a causal relationship in 7% (26/354) of instances across the 44 traits  and hemoglobin levels  . The MR Egger analyses supported the causality of female T to ER+ breast cancer , whereas prostate cancer risk was linked to SHBG levels . Additionally, SHBG decreased lymphocyte count in both sexes , and in men SHBG decreased risk of erectile dysfunction . Finally, we observed causality between SHBG and age at menopause , suggesting SHBG levels modify reproductive phenotypes across female lifespan.In the LCV and MR-Egger analyses, we found statistically significant evidence  Fig. . In addip = 0.036). Yet, emphasizing the intricate relationship between hormone levels and metabolism69, we for instance detected obesity and liver function related traits being causal for T in females: triglycerides increasing free T levels,  and GGT total T levels . In combination with the FinnGen data, these results thus further suggest that in most instances the connection between T and SHBG levels and complex traits and diseases is not straightforward.Overall, we detected relatively little robust statistical evidence for T levels being causal to most traits  has been proposed to modify phenotypes and diseases that differ between the sexes, due to the extensive male-female differences in circulating T levels. Besides the biological impacts of normal variation in T levels, the use of T replacement therapy in medical practice has been globally on rise, and the potential risks and benefits associated with T supplementation remain debated26. Whereas our analyses supported causality of genetically determined T, e.g., to hormone-sensitive cancers and hirsutism, this did not apply for traits such as obesity, T2D or hypothyroidism. Accordingly, we stress the complex relationship of T levels with many metabolic and endocrine traits. The results echo the epidemiological observations that lower T levels correlate with increased risk of metabolic disease in men, while in women the opposite hold true71. However, our data shows that these associations to metabolic health may reflect widespread genetic pleiotropy, implying complex etiology as opposed to T action. We further estimate that normal heritable variation in T levels—contrary to popular beliefs—has only modest effects on many phenotypes. In the light of our study, despite the potential behavioral impact of hormonal processes, for example the concept of explaining the risk for many temperamental and neurological phenotypes with heritable differences in adult T levels73 appears unsubstantiated.We observed three major themes regarding genetically determined T’s contribution to disease. First, we detected causality between higher T levels and several sex-biased phenotypes with biological links to T, but less contribution to most other phenotypes, echoing experimental data and findings from recent MR studies15 and at the genetic level, meaning that same genetic variants affect both traits. Underscoring the contribution of SHBG to total T associations, we found SHBG causal for total T levels in males. Especially for many metabolic traits, SHBG—either directly or through its metabolic network—appeared to modify some of total T’s associations and causality estimates. Consistent with potentially divergent biological effects for the SHBG bound and unbound T, we however observed distinct association profiles for total and free T fractions in men. For women, the situation was the opposite: whereas total T associations seemed largely independent of SHBG action, for many metabolic traits the high dependency of calculated free T on SHBG levels was evident.Secondly, we highlight how our data reflects the correlations between total T, SHBG and free T levels in both sexes. Total T in men correlates with SHBG levels epidemiologically75. In addition, all these traits correlate with obesity32, that in turn correlates with PCOS and T levels  are generally expected to be low, due to potentially conflicting evolutionary pressuresR2 9.2% for SHBG in males in the YFS), for example male free T PGS seemed to yield less accurate predictions (1.1% in YFS). Since the majority of the variance remains uncaptured by the PGSs, we cannot draw conclusions about the function of T based on the PGSs alone, and results may not be fully generalizable to other than European populations. Nonetheless, we seem to have been well powered to detect any substantial effects for the PGS  explained a substantial fraction of the trait variance also in younger individuals , and these did not always agree upon causality. We further note that the emphasis of our study has been on adult T levels, whereas some of the effects of T might be organizational, acting during specific developmental windows. Our setting thus may not allow for assessing the effects of fetal T exposure, which may be critical, e.g., for neurological traitsDespite these challenges, in this study we highlight several previously unreported, albeit often expected relationships with genetically determined T levels, human health, and sex differences. Besides the gained medical insight, underscoring some critical factors that should be considered when assessing these connections, our study therefore provides a novel reference point for future genetic and epidemiological studies on T.Supplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesReporting Summary


Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype—partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments.A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are  We live in a symbiotic world. It is increasingly recognized that many important traits, including human metabolism, insect diet and defense, and plant nutrient foraging, are actually symbiotic extended phenotypes governed (at least in part) by cryptic variation in their microbial symbionts  of context-dependence”  includin18–24–30–35–Medicago-Sinorhizobium interactions  , wit–ractions –47. Thesactions –, 49. In if, fix) –56. Symbf, fix) –, 57, 58,f, fix) ––61. ThisDecades of functional genetic studies have resolved several symbiosis genes, mostly those that disrupt symbiosis when knocked out , enMedicago truncatula with 191 strains of the model rhizobium Sinorhizobium meliloti, we ask whether the genetic architecture of rhizobium partner quality differs across experiments and across host genotypes , or alternatively if variation at a core set of rhizobium genes control partner quality in all contexts . If the underlying genes that contribute to variation in partner quality are largely conditional, then predicting how partner quality evolves under different conditions will be more challenging. Additionally, we examine how the three genomic elements of S. meliloti, symbiosis plasmid A (pSymA), pSymB, and chromosome, contribute to partner quality variation across two host genotypes and in multiple replicated experiments, validating the functional division of labor of these elements in nature. Although the canonical symbiosis genes located on the symbiosis plasmids are likely to contribute, additional loci on the chromosome may be just as important for determining quantitative variation in partner quality. Finally, we suggest candidate metabolic functions and genetic loci that are responsible for both context-dependency as well as universally beneficial effects that are consistent across experiments.Here, we conduct multiple GWAS to study the complex genetic architecture and layers of context-dependency in the G→P map of partner quality. Because symbiotic extended phenotypes by definition have a multigenomic basis , these traits are likely governed by many genomic variants, each with small effects. Results from a single GWAS may therefore be underpowered to identify the causal loci governing these traits. By examining associations that overlap across multiple GWAS using different host and symbiont genotypes, we can nonetheless identify candidate loci that are consistently associated with traits regardless of environmental or genetic contexts, and thus, are good targets for subsequent functional validation, genetic engineering, and coevolutionary studies. Using data from four separate GWAS that paired two lines of the host Sinorhizobium meliloti isolated from natural populations in the native range, we conducted four separate GWAS that involved inoculating plants individually with each strain across two “replicate” experiments for two host lines of Medicago truncatula: for experiments 1 and 3, we used host line DZA 315.16 (hereafter abbreviated as DZA) planted in March 2018 and September 2018, respectively, while for experiments 2 and 4, we used A17 planted in May and November of the same year, respectively. Conditions between replicate experiments were kept as consistent as possible, the only major difference being the time of year they were planted. Our phenotypic and genetic analyses based on these four experiments suggest important roles for context-dependency in the genetic control of symbiotic extended phenotypes. Based on our permutation method to determine significance, we found a total of 1,453 variants  of strain means between experiments within a host line were generally significant . Thus, the “E” term in our models of G × E captures any real differences in abiotic conditions between experiments in addition to experimental reproducibility. Indeed, we found that environmental dependence was a pervasive pattern at both the phenotypic, genomic, and individual gene levels, suggesting that different sets of rhizobium genes contribute to symbiotic partner quality under different environmental conditions. At the phenotypic level, ANOVA indicated abundant genetic variance in partner quality traits, including both significant main effects of strain as well as strong strain-by-experiment (G × E) interactions see . Cross-enificant , consistnificant .At the genomic-level, our evidence indicates that G × E was driven by small-scale shifts in the identity and estimated allelic effects of individual loci across environments rather than large shifts in which genomic elements have contributed to partner quality variation , as was the “Oxidative phosphorylation” KEGG pathway , while the GO terms “3,4dihydroxybenzoate catabolic process” and “transmembrane transport” were marginally significantly enriched , as were the related UNIPROT keywords for “transcription regulation” (FDR corrected P = 0.00310) and “DNA-binding” , we used correlations of the estimated allelic effects to assess the degree to which the individual effects of rhizobium alleles on partner quality were consistent in direction and/or magnitude, or whether they depended on the experiment. When variants were significantly associated with partner quality in both experiments , while bwithin at least one of the four experiments when each was mapped independently, indicating abundant overlap in the genetic architecture of both within- and among-experiment partner quality variation (Data Set S6 in reference traA” (P = 0.0292) and “mobA/mobL protein” (P = 0.0292), as well as several INTROPRO terms containing “tetratricopeptide-repeat” (p range: 0.006 to 0.0310), among others  genes to be those that were associated with partner quality in y in A17 . These gP = 0.00771), while GO terms for “transferase activity, transferring glycosyl groups” were marginally overrepresented  and exoW  and “cellular amino acid metabolic process”  and “Tetratricopeptide repeat-containing domain” (P = 0.00160) as well as UNIPROT keywords “Transmembrane” (P = 0.0190) and “Transmembrane helix” (P = 0.0407), though none of these terms were significant after FDR-correction  putatively involved in calcium/proton exchange. Though cax genes are widespread in bacteria and eukaryotes , potE (Q92ZU0), and msbA1 (Q92VZ4)—in addition to the nodulation protein glmS/nodM (Q92ZK3) and the universally associated cax transporter (discussed above). The role of potassium transporters such as kdpA in osmoregulation during symbiosis is not well understood  is a predicted amino acid decarboxylase in the putrescine biosynthesis pathway, potentially suggesting a role for variation in putrescine metabolism, which is known to vary in S. meliloti  experiments highlights the existence of segregating natural variation in several interesting metabolic pathways, only some of which have established roles in symbiosis. We found five loci annotated as involved in transmembrane transport  that catalyzes a precursor of both peptidoglycan and Nod factor in the glucosamine biosynthesis pathway. This locus is located in the symbiosis gene region of pSymA, though a paralog exists on the chromosome (Q92PS4)  loci (traA2 on pSymB and traG on pSymA), potentially part of a type IV secretion system (T4SS) responsible for targeting proteins to host cells  . Knockou alfalfa  and Rhizinosarum ; togetheinosarum , our stust cells , 90. WhiUnderstanding our symbiotic world requires a genetically accurate appreciation of the symbiotic extended phenotypes upon which selection acts. While evolutionary ecology has long recognized the importance of genetic variation in symbiosis while ignoring the underlying mechanisms, functional geneticists have traditionally resolved mechanisms without considering natural variation. Synthesis of these two perspectives has started to resolve ecologically relevant quantitative variation at the nucleotide level 45–, 91, 92.93–Ecological effects of context-dependency in mutualism have been recognized for a long time, i.e., traits and mutualism benefits often shift across environmental conditions such as nutrient availability or light environments –99, alth–102–S. meliloti on five M. truncatula lines, Batstone et al.  depend on the environment and, therefore, that a nuanced understanding of how complex traits interact with environmental variables will be necessary for many of the lofty goals in plant microbiome and symbiosis research , 35. ThiUncovering the complex genetics of how two (or more) genomes interact with each other to generate trait variation is an important step to better understanding how these traits (co)evolve  and how Medicago HapMap collections, which maximize host genetic diversity using a range-wide sample. Our study focuses on the segregating natural variation within rhizobia at a smaller geographic scale, a scale at which pSymA and pSymB segregate , our results present an important juxtaposition, as the abundant and context-dependent genetic variation characterized in detail here occurs within a set of 191 rhizobia strains with >98% average nucleotide identity . At the same time, functional genetic studies have made much progress identifying loci critical to symbiosis establishment and downstream processes by creating knockout or knockdown mutants and comparing their associated symbiotic phenotypes to a wild-type strain , but thP-values, but it does so at the cost of inducing inflation in the number of significant loci, likely due to strong linkage disequilibrium present among our strains. Any biological conclusions about any specific loci highlighted in our study should thus be made with caution. Rather, linking together the overlapping universal loci we identify here with transcriptomic data sets and in silico modeling of both plant  were grown in single inoculation with each of 191 S. meliloti strains, with three to four replicates per strain per experiment . Experiments were planted in 2018, in March (I: DZA), May (II: A17), September (III: DZA), and November (IV: A17). Uninoculated controls (40 per experiment) were included to gauge contamination, which was minimal overall, and limited to the first two experiments , and performed four separate GWAS that accounted for rhizobium population structure and included only unlinked variants. We determined which variants were significantly associated with partner quality using a permutation method . Second, we used a conservative approach to call “G × G genes” as those mapped in both experiments for one host genotype but neither of the experiments for the other host genotype . Third were genes significantly associated with partner quality in a single experiment, and never in another . Finally, we conducted candidate gene functional analyses to understand how loci within different categories differed from one another functionally, or whether they were part of the same networks/metabolic pathways.https://github.com/rtbatstone/complex_genetics_of_symbiosis). Raw sequence reads and assemblies for the 191 strains are available at NCBI (BioProject accession no. PRJNA501532), with metadata available in Riley et al. (Strains and plant lines are available upon request. All raw data and analysis code are available on GitHub (see “Complex_genetics” folder at y et al. .


This article aims to develop a new approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals. Genome-wide associations analysis revealed the following potential loci of quantitative traits on cattle chromosomes for chest width, chest girth, and meat output on bones.In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip , containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals. Growing population demand for meat and dairy products has led to the rapid growth of animal husbandry worldwide. To increase productivity in animal husbandry, an objective and daily assessment of the productivity of animals in industrial livestock complexes is necessary. Given the complexity of traditional approaches to expert assessment, developing new methods for automatic assessment and forecasting of animal productivity, considering modern technologies in the field of engineering is a significant scientific and practical task ,2. This Genome-wide association studies (GWAS) have been a useful tool in medical practice for several years ,4. With Aberdeen Angus is considered one of the best breeds among hundreds of others for marbled beef production, characterized by an ideal structure, excellent taste, and pleasant aroma. The meat of the Aberdeen Angus breed, along with other meat breeds, is considered the best for food purposes . CurrentIn this regard, the purpose of this study was to analyze the genome structure of an experimental group of young cattle of the Aberdeen-Angus breed based on GWAS phenotypic parameters of the exterior.Computer vision can provide the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features.A test analysis of genome-wide associations revealed the potential loci of quantitative traits on cattle chromosomes for chest width, chest girth, and meat output on bones.Database creation: the database contains raw RGB-D images, point clouds, and live weight, measurements obtained from a 3-D model , genotyping data, slaughter characteristics—live weight, meat yield, stress losses, characteristics of the front quarters (19 parameters), characteristics of the rear quarters (15 parameters), characteristics of offal (22 parameters) for 96 Aberdeen-Angus cattle.The main contributions of this article are as follows:The study offers an innovative approach to developing technology for assessing and predicting animal productivity based on automated body measurements  using a 3-D model of Aberdeen Angus cattle obtained from multiple depth cameras. The proposed technology is based on methods of non-contact 3-D reconstruction of the animal model, productivity parameters, genetic characteristics, and animal health, that are closely related to the features of their constitution and exterior.Currently, DNA technologies are widely used to develop methods for managing the flow of genetic material and conducting genetic monitoring to assess the degree of polymorphism. Progress in the development of genetic research is due to the presence of informative genetic markers. In the last decade, a trend has developed in world science called Marker assisted Selection (MAS) ,18. ReseMany factors influence the efficiency of animal production; one of the most significant is the genetic potential of animals ,23. The Thus, genotyping of herds by individual DNA markers makes it possible to create groups of animals that differ in productivity potential, quality composition of products, level and direction of metabolism, and energy in the animal body .For the study of beef cattle, the main task is to control the development of muscles and, as a result, the quality of meat. Beef quality is a complex variable phenotype detected only after slaughter ,28. To dThe emergence of genomic selection with correspondingly improved accuracy prediction will accelerate the introduction of elite genetics in enhancing breeding efficiency in beef cattle populations. The existing problems associated with this approach can be overcome long-term by increasing international collaborative efforts. Still, in the short term, they will not eliminate the continuing need for accurate measurement of the primary phenotype ,32.The young cattle of the Aberdeen-Angus breed (n = 96) were selected as the object of the study. At the time of the experiment, the age of the animals was 16.5 months; the live weight was 614.9 kg. All experimental procedures and ethical standards were approved by the Committee for Control over the Maintenance and Use of Laboratory Animals in accordance with the “Policy of the Scientific Center for Work with Laboratory Animals” of the Federal State Budgetary Scientific Institution “Federal Research Centre of Biological Systems and Agrotechnologies of the Russian academy of sciences”. Extracts from the minutes of the meeting of the Commission for control over the maintenance and use of laboratory animals No. 4 dated 17 January 2021 were received on 20 March 2022. During the research, measures were taken to ensure a minimum of animal suffering and to reduce the number of experimental samples studied.The animals were kept in a separate group at a feedlot in the Ramonsky district of the Voronezh Region, Russia. The animals were of the same sex and the same age. The feeding and maintenance conditions were the same for the whole group. Upon completion of fattening, the animals were slaughtered at a commercial slaughterhouse owned by a meat processing plant. Animals were identified using an RFID chip, and carcasses were identified using tags and cooled for 24–48 h after slaughter. After cooling, the carcass was cut into marketable pieces.The morphological characteristics of the animals were measured in accordance with the guidelines issued by the Committee. Data collection in this study was carried out without restriction on cows. Therefore, this study does not require the approval of the Animal Care and Use Committee, which eliminates the need for the approval of environmental monitoring or animal sciences.TA database of phenotypic traits of Aberdeen-Angus bulls was formed to conduct genome-wide associative studies. The indicators were divided into external ones—height at the withers and in the sacrum, chest width behind the shoulder blades, chest girth, and meat output on the bones.Measurements of exterior features and meat productivity were made using a system of accurate measurement of the body of live cattle using three-depth chambers and non-rigid three-dimensional shape restoration ,34,35.A database containing the following data for each of the 96 animals was collected: RFID chip number, RGB images, depth maps, and point clouds; live weight, linear measurements obtained from a 3D model , genotyping data, slaughter characteristics—live weight, meat yield, stress losses, characteristics of the front quarters (19 parameters), characteristics of the rear quarters (15 parameters), characteristics of offal (22 parameters). This database is publicly available .Genomic DNA was extracted from the whole blood using «DNA-Extran» . The concentration of genomic DNA was detected by Qubit , and the quantity of DNA was estimated by Nanodrop ND-1000 . The 96 animals were genotyped with the Illumina BovineHD Beadchip  following standard procedures of the manufacturer.p-value < 10–6 (--rhb). The unmapped SNPs and SNPs present on X and Y chromosomes and mitochondrial DNA were removed. SNPs located on autosomes were put to analysis. The quality control procedure was carried out using PLINK . The PliAdditionally, a heterozygosity test was conducted to determine which samples deviate from the average value by more than 3 standard deviations to exclude them from the analysis. Finally, the principal component method was used to estimate the population stratification, Principal component analysis (PCA) .The minor allele frequency (MAF) is the frequency of the least common allele for that SNP in the population. The MAF for each autosome and the overall mean MAF in cattle breed were estimated by using PLINK. The significance of breed differences in MAF was done using the SPSS package. The MAF values of all the SNPs were classified into four different categories viz. monomorphic/fixed SNPs (MAF ¼ 0), rare SNPs (MAF > 0–< 0.05), intermediate SNPs (MAF ≥ 0.05–< 0.1), and common SNPs (MAF ≥ 0.1–≤ 0.5).Multiple linear regression analysis implemented in Plink 1.90 was used to identify associations of SNP markers with the studied phenotypic indicators. GWAS was performed for the six morphometric traits using PLINK . A lineap-value in GWAS was calculated using the formula:Since many hypotheses are tested simultaneously during the SWOT analysis , the correction was made for multiple hypothesis testing. In this study, the Bonferroni correction was applied, and the threshold for the p < 4.5 × 10−7 were considered statistically reliable. Data visualization was carried out in the qqman package using the R programming language [Thus, during the GWAS, associations with language .The data collection unit was installed in the passage to the hall with a feed system. All measurements were carried out on a walking animal from three points of view since it is impossible to require the animal to stop and remain motionless. Two RGB-D cameras were located to the right and left of the animal aisle at a distance of about 2.0 m; the third Kinect camera was located above the aisle at a height of about 3.0 m above the ground. The installation used three identical Microsoft Kinect v2 cameras  that receive RGB and depth images from the left, right, and upper sides of the animal. Each depth camera was connected to a laptop, and all laptops were connected to a local network. Synchronously captured images in RGB-D format were recorded on the corresponding laptop for each camera. Data collection and storage were implemented based on Software Development Kit 2.0 (SDK). Each camera initialized by the start signal starts capturing frames at 30 Hz. The time on the laptops was synchronized, and the best match of the point cloud could be selected within the shortest time intervals between the three devices. Transformation matrices using external calibration were calculated distances between different cameras to align the point cloud. The resolutions of RGB images and depth images are 1920 × 1080 and 512 × 424 pixels, respectively. More information can be found in .The point cloud fusion and 3-D reconstruction of live cattle were carried out using the proposed automated computer vision system  that canFinally, the point cloud fusion was elaborated to generate the 3-D cattle model. withers height is defined as the height of the highest point at the withers;hip height in the sacrum is the height of the highest point at the sacrum;chest width is defined as the width vertically tangent to the posterior corner of the shoulder blade;chest girth is defined as the vertical tangent to the back corner of the shoulder blade.The estimated body measurements were according to the following definitions:The proposed 3-D animal automatic measurement system was implemented using three RGB-D cameras . With a The obtained 3-D models of animal bodies made it possible to collect measurements of live weight, withers height, hip height, chest width, and chest girth. In addition, the process of cutting carcasses at the meat processing plant provided valuable data on meat output on bones. All the listed data are presented in Based on these results, indicators characterized by multiple gene action were selected for testing genome-wide associative studies.The created database is open and accessible to the research community . The datp < 0.001) and r = 0.100. These parameters were selected for further analysis.At the initial stage, the relationship between the main features of the exterior was evaluated. The results showed that the live weight, withers height, and rump of the animal had a weak and negative relationship with meat output on the bones , which indicated the absence of a reliable relationship. At the same time, such signs as chest width and chest girth were significantly correlated with meat output, respectively, r = 0.279  and subsequent genotyping of a large number of animals made it possible to conduct a large-scale analysis to understand the biological processes underlying the variability of animal populations. The availability of SNP panels for the entire genome made it possible to implement genomic prediction in many livestock species ,41. UnliDuring the work, it was found that the indicators of meat cuts had a higher variability relative to the yield of offal and exterior parameters.In the series of observations, correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals. Data regarding the results of multidimensional scaling are similar to those provided by the literature , which eThe results obtained using GWAS showed that the resulting additive genetic variance is unevenly distributed across the genome. Moreover, some regions explain a larger share compared to others. GWAS identified the following potential loci of quantitative traits on cattle chromosomes for: live weight—BTA1, 3, 5, 15, 16, 17, 19, 20, 23, and 29; withers height—BTA2, 3, 5, 6, 7, 10, 12, 14, 15, 21, 22, 24, 26, and 29; hip height—BTA2, 3, 5, 6, 8, 9, 10, 12, 20, 21, 26, and 29; chest width—2, 3, 5, 7, 8, 11, 12, 13, 16, 20, and 21; chest girth—BTA1, 2, 5, 7, 8, 11, 16, 17, 19, 21, 22, and 23; meat output on bones—BTA1, 2, 3, 5, 6, 7, 9, 11, 18, 20, and 29, which is partially consistent with the data of ,47,48,49In this paper, the genetic parameters of phenotypic features of the exterior 3-D animal model of the Aberdeen-Angus breed were evaluated by the method of genome-wide associative research (GWAS). GWAS provides valuable estimates of genetic parameters and key information about the regions underlying the variability of the traits discussed. Correlation analysis showed a positive, reliable relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.The important regions identified in this study can provide valuable biological information to improve the accuracy of genomic selection in breeding programs for Aberdeen Angus beef cattle. These studies are planned to be applied to other cattle breeds. The main motivation for conducting genome-wide association studies (GWAS) in domestic animals, such as beef cattle, is the detection of genes or causal mutations that contribute to the phenotype of economically important traits. Such results may be important for improving the breeding value assessment and our understanding of the mechanisms underlying the long-term selection response in the artificial breeding program .


C. canephora clones for growth  and yield traits; estimate genetic parameters of these traits; and determine the phenotypic and genetic associations between these traits to guide future crop improvement efforts. The productivity of 56 coffee clones was assessed from 2012 to 2020 in a clonal experiment planted in a randomized complete block design with three replicates. Each plot consisted of eight plants spaced at 2 × 3 m at the Cocoa Research Institute of Ghana. There were significant (p < 0.001) differences among clones for all the traits assessed. Broad sense heritability was low-to-moderate for all the traits evaluated with the highest (0.34) observed for cumulative yield (CY). Cumulative yield was genetically correlated (p < 0.001) with span and number of laterals (NOL). For the growth traits, NOL was the most strongly associated with CY . The results revealed that selection based on early years’ yield (MY1) could be as effective as selection based on CY . Our findings indicate that there is significant genetic variation among the test clones for the parameters assessed and presents a good opportunity for future variety development.Effectiveness of plant improvement programs, especially in perennial crops like coffee, may be improved by knowing the level of genetic variation which exist within a breeding population coupled with the knowledge of estimates of genetic parameters for key agronomic traits. Thus, this study was conducted to evaluate  Genetic correlation; Genetic parameters; Genetic variation; Germplasm; Heritability. It grows typically in tropical and subtropical countries worldwide  and C. canephora Pierre  are the two widely cultivated species of the 126 species classified under the genera Coffea  which is predominantly self-pollinating whereas C. canephora is a diploid species (2n = 22) which is mainly out-crossing. Robusta coffee in comparison with Arabica coffee is known for its better productivity and higher caffeine content, enhanced growth at lower altitude, and better tolerance to biotic and abiotic stresses  recorded in on-station trials  clones that were derived through ortet selection based on yield of earlier hybrid experiments at the Cocoa Research Institute of Ghana (CRIG) were used in the study. The ortet selection process followed a procedure where the best performing plants in terms of growth and yield from the best families of previous hybrid trials were selected and cuttings were obtained from them to produce the clones (obtained through rooting of single-node cuttings in propagators) evaluated in this study. The rooting media in the propagators was sand and rice husk mixed in a 1:1 ratio. After six months of growth in the poly bags at the nursery, the rooted seedlings were field transplanted.2.2Gliricidia sepium was planted between rows at a spacing of 4 × 6 m to provide shade which was managed by pruning yearly. Following the 2 × 3 m planting distance used in the experiment, the total number of trees per hectare was 1667. During the field evaluation, each test plant grew on a maximum of two stems. Field assessment of the test plants were conducted following standard procedures for Robusta coffee cultivation in Ghana (−1) was measured over a 6-year period (2015–2020).The field experiment was carried out at CRIG which is located at New Tafo-Akim  in the Eastern Region of Ghana. The soil at Tafo is sandy loam, brown to yellowish red, well drained, and developed in situ from weathered materials of hornblende granodiorite and classified as Haplic Luvisol . The infin Ghana . The tes2.3Using electronic calipers, stem diameter of each test plant at 10 cm above the soil surface was measured. Span (m), a measure of canopy width was taken where tree canopy was the widest. For each tree, the number of laterals was counted at each data collection time. In instances where a test plant had more than one stem, stem diameter was estimated following 2.4ij is the phenotypic measurement of the ith clone in the jth block for trait Y, μ is the population mean, gi is the effect of the ith clone, bj is the effect of the jth block and eij is the error term. Additionally, in accounting for uncontrolled heterogeneity due to random factors, a linear mixed model analysis procedure was explored where clone and block were considered as a random and fixed factors, respectively. As a measure of plant vigor, data on stem diameter used in the analysis was estimated as the change between final and initial trait measurements in 2015 and 2012, respectively. Number of laterals and span data used in the analyses were restricted to the final vegetative trait measurements in 2015 which was the start of the reproductive growth phase of the crop. The ANOVA following tests for normality  was carried out with the ADEL-R statistical tool  based on a linear model as in cal tool .rg) between traits were estimated following Genetic correlations (2) for each trait on an error plot variance (entry-mean) basis following σ2g and σ2e are genotype and error variance components, respectively, and nr is the number of replications. Only broad sense heritability estimations were possible due to the clonal nature of the genotypes assessed in the trial.Variance components were assessed and used to estimate broad sense heritability  for each trait was estimated following Heritability and correlation analysis were carried out using the METAR-R statistical package .33.1A mean rainfall of 1473.7 mm per annum was received during the study period. The least and highest amount of rainfall were received in the years 2015 (1088.1 mm) and 2014 (1748.8 mm), respectively . The acc3.2p < 0.001) difference among the Robusta coffee clones for all the assessed traits (p < 0.01) block effect for all traits (p < 0.01) effect of clone, year and clone × year interaction for all traits when a combined ANOVA was conducted (data not presented). The linear mixed model analysis where clones were considered as random factors also revealed significant differences among the clones for all the traits measured (Table S1). Generally, for all traits, it was observed that the agronomic performance of the clones for the most part were slightly over estimated in the analysis of variance where the clones were considered fixed compared to the linear mixed model analysis approach (Table S1). The opposite was however, observed for some clones. Regardless of the plasticity in trait performance estimation between the two analyses approaches (simple ANOVA vs a linear mixed model approach), the rankings of clones for each trait did not change. A clear example were clones H498 and E139 which were the least and highest yielding clones, respectively in terms of cumulative yield for both models of analysis. Given the similarity between the two analysis approaches, the proceeding report of trait performance of the clones was based on the simple ANOVA output.Analysis of variance (ANOVA) revealed a highly significant  better than the best standard clone (E139). On the other hand, five clones  were found to be significantly better in terms of SDI than the second standard clone (E138) over a 4-year (2012–2015) vegetative growth period (p > 0.05) from the clone (H459) which had the least number of laterals (Stem diameter increments (SDI) ranged from 27.6 mm (H317) to 44.4 mm (E139 × C134) among the 56 clones evaluated, with the standard clones (E138 and E139) recording SDI of 33.1 mm and 37.2 mm, respectively . Distribh period . Span valaterals .Table 3M−1 in clones (H498 and H459) to 3.4 t ha−1 in the standard clone, E139 with a mean of 1.2 t ha−1  and 2.9 t ha−1 in the standard clone, E139  followed a similar trend varying from 1.0 t ha−1 in clones  to 3.0 t ha−1 in the standard clone, E139. Cumulative yield for all the clones over the 6-year period (2015–2020) ranged from a low of 2.5 t ha−1 in clone H498 to a high of 15.5 t ha−1 in one of the standard clones, E139  and genetic (rg) correlations among the vegetative growth  and yield  traits measured are presented in rg = -0.29, p < 0.05) and between 2016 yield and 2020 yield . In contrast, the phenotypic associations were significantly lower than their equivalent genotypic associations in most trait combinations . Among the growth parameters, the strongest association was found between number of laterals and span   for (2015 yield vs MY2) to  for   . The gend vs CY) .Table 5Grg = 0.87, p < 0.001). Similarly, selection based on average early years’ yield (MY1) may be very effective as selection based on later years’ average yield (MY2)  . The genetic correlations further revealed that selection for plants with larger span could result in plants with a moderate increase in cumulative yield . Similarly, selection for coffee trees with increased number of laterals could result in trees with a moderate increase in cumulative yield   would be as effective as selection based on cumulative yield (CY), adjudged by the relatively strong genetic correlation between MY1 and CY (< 0.001) . Also, l< 0.001) . Both po4In any crop enhancement program, knowing the genetic properties of target traits and the inter-relationships of these key agronomic traits is a pre-requisite in establishing a successful selection program. For this reason, our study sort to determine the agronomic performance and genetic variation among Robusta coffee clones in the coffee improvement program at the CRIG and decipher the genetic relationship among key traits of interest to guide future selection and hybridization efforts. Our findings build upon previous studies in coffee and other crops and will be relevant for informing future breeding strategies for Robusta coffee.Generally, it is agreed that Robusta coffee is mainly a cross-pollinated species, hence a high level of genetic variability exists in this species. In corroboration with this assertion, our results revealed the presence of considerable variation among the clones for all the traits assessed. The existence and exploitation of such sufficient variability among the evaluated clones provides immense opportunity for development of better Robusta coffee varieties in the future. The significant variability observed for the study traits were in agreement with the finding of earlier researchers who reported significant genetic variability within Robusta  and Arab−1 over a six-year yield collection period. This suggested that significant efforts need to be made in the CRIG Robusta coffee breeding program through ortet selection of clones from hybridization efforts as well as introduction, evaluation and selection of high-yielding germplasm clones to identify newer higher-yielding clones to augment existing varieties. This is paramount considering the significance of high-yielding varieties in enhancing national coffee productivity. Regardless of the best clone being one of the standard clones, six new clones  were significantly better than the standard clone, E138 with cumulative yields ranging between 8.6 t ha−1 (H1001) to 10.6 t ha−1 (A129). These promising clones together with other genotypes may be good candidates for future multi location clonal evaluations.Cumulative yield is by far an important yield parameter to be considered in determining the overall yield potential of varieties of a perennial tree crop like coffee. From this study, the highest-yielding genotype was the standard clone, E139 with a cumulative yield of 15.5 t haCorrelation analysis in plant breeding is important because it can help detect associations among target traits, identify new parental combinations in developing improved varieties and identify redundancy in trait measurements . Phenotyrg = 0.87, p < 0.001) was found between the first three years’ mean yield (MY1) and cumulative yield (CY) as well as a strong genetic correlation  between the first three years’ mean yield (MY1) and the last three years’ mean yield (MY2). Variety development in a tree crop like coffee can take several years to achieve, given how long it takes to measure yield effectively. Such a strong genetic correlation between early years’ mean yield and cumulative yield suggests that selection for yield could be practiced early in a breeding program to achieve similar gains as when selections were performed on later years’ mean yield or cumulative yield. Stem diameter increment was poorly associated with cumulative yield from the present study which is in disagreement with findings of Despite the importance of genetic correlations in any plant breeding program, there are potential trade-offs for genetic gains throughout breeding that may be targeted at developing improved varieties. A strong genetic correlation between a set of traits may result in a possible significant genetic gain in a trait that is indirectly selected for. The reverse is the case when the genetic correlations between the traits of interest is weak. This is particularly important given the usually complex nature of the covariance matrix among target traits  where thThe variance due to the environment was larger than the variance due to genetic factors for all the traits assessed in the present study, indicating the predominance of environmental variance to the detriment of genetic factors. This finding suggests that predictability of family performance would be difficult as families with good trait measurement in one year may have varied response in different seasons. Nonetheless, increased sampling of the experimental area through increased number of trees per replication may be essential to increase the accuracy of observed genotypic differences and reduce the variance due to error in future studies. The findings of our study especially regarding heritability estimates demonstrated that there may be heritable variation upon which selection could act due to changing environmental factors. However, a limitation in the present study is the lack of evaluating the genotypes in contrasting environments as well as the lack of explicit estimates of selection coefficients from fitness vs. trait values regressions that give credence and more impetus to the potential of making significant gains from selection via conventional clonal selection . Since tTo develop improved varieties, it is imperative that coffee breeders have target traits that have high heritability, positively correlate with other traits and exist in populations with significant genetic variations to allow for better trait improvement. Broad sense heritability is the amount of phenotypic variation that is due to total (additive and non-additive) genetic effects and is vital in clonally propagated crops like Robusta coffee where clonal propagation of elite individual genotypes can exploit this source of variation . Only brThere was marked variation in broad sense heritability for the five yearly harvests for yield from 2015 through 2020. The variation followed a biennial pattern where a low estimate of heritability in one year was followed by a high estimate of heritability for yield in the subsequent year. The different estimates of heritability for the six yearly harvests and cumulative yield are probably a result of differences in gene expression during plant growth and development, differences in size, vigor and early seedling growth following field transplanting as well as prevailing conditions of the environment in the year harvesting is undertaken.Estimates of genetic parameters permit the breeder to better understand the nature of the gene action involved in inheritance of traits, allow for better assessment of the progress expected with selection, as well as help to define the best selection strategies to be employed in any breeding program . EstimatGenerally, the PCV for the yield traits were much higher than GCV, leading to relatively higher GAM for the same traits. This suggests that the traits with high GAM are controlled more by additive genes as proposed by In conclusion, the present study has revealed the presence of significant genetic variability among the 56 Robusta coffee clones for key agronomic traits that could be exploited in future coffee improvement programs. For an effective selection, the combined use of heritability and genetic advance is vital for assessing the attainable level of genetic improvement from selection of target traits. The highest broad sense heritability for the growth and yield traits were observed for number of laterals and cumulative yield, respectively. This coupled with the relatively high GAM for both traits suggests that direct selection for number of laterals and cumulative yield would result in an increase in the frequency of favorable alleles for both parameters in the population that would lead to significant gains from selection in our breeding program. The significant genetic association found between early years’ yield (MY1) and cumulative yield in our study suggests that selection in early years could be as effective as selection carried out in later years, thereby facilitating the crop improvement process.5One major finding of the present study was the significant clone by year interaction when a pooled ANOVA was conducted. Also, there were differences in yield across years depicting a biennial pattern which has significant implications for improvement in selection efficiency in a crop improvement program. Considering that the clones were tested in a single location over several years in the present study, invites strongly considering whether environmental/year plasticity may be further studied in our crop improvement program and other similar programs. Better modeling of genotype by environment/year plasticity in Robusta coffee would require testing all evaluated clones or similar ones in multi environments over several years which would allow for improving selection efficiency and gains from selection for important traits in any crop improvement program . This isAbraham Akpertey, Esther Anim-Kwapong: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Paul Kwasi Krah Adu-Gyamfi, Atta Ofori: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data included in article/supplementary material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


CADM2) gene has appeared among the top associations in a wide range of genome-wide association studies (GWASs). This study aims to: (1) examine how widespread the role of CADM2 is in behavioural traits, and (2) investigate trait-specific effects on CADM2 expression levels across tissues. We conducted a phenome-wide association study in UK Biobank  on 242 psycho-behavioral traits, both at the SNP and the gene-level. For comparison, we repeated the analyses for other large (and high LD) genes. We found significant associations between CADM2 and 50 traits , many more than for the comparison genes. We show that many trait associations are reduced when taking geographical stratification into account. S-Predixcan revealed that CADM2 expression in brain tissues was significantly associated with many traits; highly significant effects were also observed for lung, mammary, and adipose tissues. In conclusion, this study shows that the role of CADM2 extends to a wide range of psycho-behavioral traits, suggesting these traits may share a common biological denominator.The cell adhesion molecule 2 (The online version contains supplementary material available at 10.1007/s10519-022-10109-8. CADM2). Common variations  in the CADM2 gene have been implicated in various traits, including substance use traits  have identified tens of thousands of associations between genetic variants and a range of human behavioral and physical traits. One gene that has popped up surprisingly often in behavioral GWASs is the cell adhesion molecule 2 gene (CADM2 encodes a member of the synaptic cell adhesion molecules (SynCAMs) involved in synaptic organization and signalling, suggesting that alterations in CADM2 expression affect neuronal connectivity. CADM2 is expressed more abundantly in brain tissue than in other tissue and in particular in areas important for reward processing and addiction, including the frontal anterior cingulate cortex  with a comprehensive selection of psycho-behavioral phenotypes as measured in the UK Biobank cohort. Results provide insights about whether the role of CADM2 is confined to a specific set of traits or is involved in a wider range of phenotypes. This will inform future studies on the function of CADM2 and the neurobiological underpinnings of different psycho-behavioral traits. An additional advantage is that the multiple testing burden is reduced as compared to genome-wide studies, resulting in higher statistical power.In this study we performed a phenome-wide association analysis (PheWAS), in which we tested for associations of CADM2 region 250 kb up- and downstream  and selected 4,265 SNPs with missingness rates < 5%, minor allele frequency > 1%, and p-value for violation of Hardy–Weinberg equilibrium above 10–6 ; with the only difference that we included all HRC imputed SNPs, whereas Abdellaoui  and the number of traits, resulting in 0.05/(133*242) = 1.55E−06. For the gene-based test we used a threshold of 2.62E−05, corresponding to 0.05 divided by the total number of genes included in the test . To provide an estimation of the effect size of the top-SNP for each trait, we used The SNP-based association analyses were performed in fastGWA  reached significant associations at a genome-wide corrected CADM2 is located in a long-range linkage disequilibrium (LD) region, making it potentially unfeasible to adequately control for population structure with PCs. Also, there may be genetic signal picked up by genetic association analyses that is due to social stratification, which will not be accounted for by these 25 PCs. We therefore performed a sensitivity analysis in which we—in addition to the 25 PCs—controlled for the participants’ region of birth and region of current address (see Supplementary methods). Controlling for these geographical covariates attenuated the association results: from the 50 significant trait associations at the gene level, 26 were no longer significant, and on average the betas of the top-SNPs within these genes were attenuated with by 16% . These findings implicate that  stratification introduces regional-level gene-environment correlations that affect the genetic association results , repeated the SNP-based analysis for these genes and compared the number of traits with significant associations. Most of the random comparison genes contained fewer than 5 SNP-trait associations, with an average of 2.6 associated traits per gene and a maximum of 13 . We additionally made a comparison with five large genes from regions with a similar level of LD as the CADM2 region . Results from these comparison analyses show that the high number of associations discovered for CADM2 is exceptional .We assessed whether the high number of associations discovered for e et al. ). The nuCADM2 SNPs that showed the highest number of significant trait-associations  clustered around loci at 85.53 and 85.62 Mb. As can be seen in Fig. 2 < 0.01, distance > 250 kb) significantly associated with at least one trait cluster in the middle of the gene, a region rich in expression quantitative trait loci (eQTLs).The CADM2 expression levels in 17 brain and non-brain tissues (see Supplementary Methods). From each trait category  we selected the trait with the strongest association with CADM2. For all traits we found significant associations with CADM2 expression in multiple tissues , in range with what is normally found for single variants. Few associations were found in the social interaction, sleep, traumatic experiences, and occupational categories. Also, there were not many mental health traits that showed an association (8 out of 52 traits). It is interesting to note the significant associations with worry and nervousness-like traits in the absence of association with other depression- and anxiety-related traits. There may be something specific to these seemingly overlapping traits, translating to distinct biological pathways.This PheWAS showed that p-value of the gene-based test was moderate and significant, r = − 0.38 (p = 1.42E−9) showing that well-powered traits were more likely to result in a significant association. It is clear that high power was a requirement: the effect sizes of CADM2 were diminutive, as is expected for single genes and complex traits. Also, our tests were limited to the psycho-behavioral traits measured in the UK-Biobank; inclusion of more measures, such as longitudinal or non-self-report measures could contribute to a more complete picture. Still, the range of tested traits was quite broad and enabled us to discern interesting patterns.It needs to be noted that sample sizes for the phenotypes differed substantially , and as such, it is possible that the pattern of associations was driven in part by differences in power. The correlation between sample size and CADM2 and this spectrum of psycho-behavioral traits in terms of neurobiological mechanisms. For example, it could be that CADM2 is important for the learning aspects of behavior, given its role in synaptic connectivity. Speculatively, CADM2 could then contribute to reward-learning and associative learning, giving rise to risky behavior, substance use, and other kinds of behaviors that involve such processes Supplementary file2 (DOCX 31 KB)Supplementary file3 (XLSX 21125 KB)Below is the link to the electronic supplementary material.