P < 0.001). Job attainment characteristics were genetically positively correlated with positive health and well-being outcomes , similar to other SES indices; however, the genetic correlations exhibited opposite directions for physical demands (|rg|: 0.14–0.51) and were largely negligible for emotional demands. By adopting a finer-grained approach to capture specific job attainment phenotypes, our study represents an important step forward in understanding the shared genetic architecture among job attainment characteristics, other SES indices, and potential role in health and well-being outcomes.Job attainment is an important component of socioeconomic status (SES). There is currently a paucity of genomic research on an individual’s job attainment, as well as how it is related to other SES variables and overall well-being at the whole genome level. By incorporating O*NET occupational information into the UK Biobank database, we performed GWAS analyses of six major job attainment characteristics—job complexity, autonomy, innovation, information demands, emotional demands, and physical demands—on 219,483 individuals of European ancestry. The job attainment characteristics had moderate to high pairwise genetic correlations, manifested by three latent factors: cognitive, emotional, and physical requirements. The latent factor of overall job requirement underlying the job attainment traits represented a critical genetic path from educational attainment to income ( SES has been conceptualized and examined as a composite of educational attainment, occupation or job attainment, and income to reflect one’s social status5. Job attainment has been suggested as a critical structural link between educational attainment and income6. Current genetic research, however, has predominantly focused on educational attainment and income8, resulting in the limited understanding of the whole spectrum of SES and their relations with well-being. The current study aims to offer insight into the genetic architecture of specific job attainment characteristics, as well as the shared genetic basis between job attainment with other SES indices, and well-being, and thus may contribute to the literature on SES and occupational health research.Work represents an indispensable area of life for most individuals in modern societies. Job attainment, therefore, is regarded as a crucial component of socioeconomic status (SES) that has been widely investigated as an important predictor of health and well-being10. The dominant perspective assumes that the characteristics of the job that one holds are mostly determined by environmental factors, such as occupational and organizational contexts, and thus has overlooked the possible role of genetic factors through processes of self selection and occupational selection11. Recently, twin studies have demonstrated that genetic factors explained a substantial portion of the variance in one’s job attainment characteristics such as job demand, autonomy, and complexity, with heritability estimates at approximately 30%12. Furthermore, phenotypical correlations between job characteristics and well-being were partially explained by their shared genetic influences12.Job attainment characteristics have been mainly investigated in the literature as work contextual factors that influence individuals’ psychological and physical well-being16. We constructed the phenotypes using information from the U.S. Occupational Information Network (O*NET), a comprehensive occupational dataset that has been used across countries18, by matching the Standard Occupational Codes in O*NET to the occupational codes adopted in the UK Biobank. We performed genetic exploratory factor analysis among the job attainment characteristics, and genetic sharing among the three SES variables: education attainment, job attainment, and income. We conducted genomic structure equation modeling and path analyses to examine relationships among these SES variables at the whole genome scale. Furthermore, we explored genetic correlations between job attainment characteristics and major health and well-being outcomes, accounting for intelligence—a common factor shared among SES variables.In this study, we first conducted GWAS analyses on six commonly examined job attainment characteristics: job complexity, autonomy, innovation, and three types of job demands , which represent various aspects of job attainment20, a dominant model proposed for occupational health and job design, posits that job demands and job autonomy/control are crucial job characteristics that influence workers’ health and well-being. Meanwhile, omnibus characteristics  also constitute an important aspect21. However, such information was not directly recorded in the UK Biobank data. We thus linked the four-digit Standard Occupational Codes in the UK Biobank data to occupation codes from O*NET to extract useful information on both omnibus and concrete job attainment characteristics. Six commonly used job attainment characteristics from the O*NET database were assessed: job complexity, autonomy, innovation, and three types of job demands  . Physical demands were negatively correlated with other job characteristics (r − 0.21 to − 0.33), except for job complexity (r 0.32).In our analysis, we included 219,483 European ancestry participants in the UK Biobank with matched codes from the O*NET database , indicating that the slight inflation of λGC was due to the presence of polygenic signals, rather than population stratification or cryptic relatedness26. Thus, no further adjustment for λGC was performed in our analyses.We performed GWAS for each job attainment trait on 19,400,443 genetic variants passed quality control (QC). The genomic control inflation factor λP < 8.33 × 10−9 after multiple testing correction; Supplementary Table P = 1.90 × 10–16) on chromosome 3 for information demands, autonomy, innovation, and complexity. Genetic effects of the top variants in the age subgroup are presented in Supplementary Table P_dif < 0.05). After accounting for multiple testing, none of these SNPs remained significant at P_dif < 3.13 × 10–3. We evaluated associations of these identified variants for job attainment in the UK Biobank follow-up data  and the Add Health Wave V data (N = 3817). Lead SNPs at the four loci showed nominal significance at P value less than 0.05 . These estimates were in line with the reported estimates for social and behavioral phenotypes such as risk tolerance at 4.6%27, household income at 7.4%7, neuroticism at 10.8%28, but substantially lower than that for intelligence at 19%29.Common variant heritability 30. We found that pairwise genetic correlations (rg) were strong and positive from 0.87 to 0.96 for complexity, autonomy, innovation, and information demands  within each job attainment trait using LDSC regression. Between-sex genetic correlations were moderate to high for autonomy (rg = 0.90), innovation (rg = 0.92), information demands (rg = 0.90), emotional demands (rg = 0.76) and physical demands (rg = 0.88), with the lowest for complexity , indicating a common genetic variant largely shared between males and females for these five phenotypes.We first performed GWAS for each job attainment phenotype in males and females separately. Based on sex-specific GWAS summary statistics, we estimated between-sex genetic correlations (P_dif < 1.32 × 10–3), the associations were present in females, but not significant in males, for complexity, autonomy, and innovation (one locus respectively), and information demands , but none showed genome-wide significance at least in one sex. The underlying mechanisms for these loci and these sex differences remain to be explored. It is warranted to futher validate these top loci showing different gender effects in future research.To detect SNPs with the different effect sizes across sexes from sex-stratified regression coefficients, we further computed per-SNP z scores of those top index SNPs showing genome-wide significance at least in one sex for each phenotype. After accounting for multiple testing 33 to investigate the possible latent factor structure of these six job attainment characteristics. Both phenotypic and genetic EFAs generated three latent factors . Meanwhile, in genetic EFA, the cognitive job requirement factor was related to all the six job attainments. The physical requirement factor was a stand-alone factor in the phenotypic EFA, and it contributed substantially negatively to the cognitive job requirement factor in genetic EFA (factor loading in genetic vs. phenotypic EFA − 0.89 vs. − 0.12). Altogether, three latent factors explained 98% of the genetic variation across the six traits.Emerging evidence suggests that major job attainment characteristics can be manifested by different latent factorsP value thresholds for variants were examined and the optimal model for each job attainment trait was selected based on the R2. PGSs were significantly associated with job attainment levels for all the six phenotypes in the UK Biobank follow-up sample , suggesting the robustness of the GWAS findings. PGS accounted for phenotypic variance in a range of incremental R2 from 0.15 to 0.91%, adjusting for age, sex, and top PCs, indicating that PGS alone had significant but small predictive value for the job attainment phenotypes in the two replicaion samples. Yet, this does not necessarily mean that PGSs were not useful, because the findings reflected only main effects of the genetic variants. Future research may examine how PGSs interact with environmental factors in shaping job characteristics and the nomological networks for them beyond job characteristics.To test whether the aggregate estimates of genetic effects correlated with job attainment, we constructed PGS for each job attainment trait in the UK Biobank follow-up and Add Health data. A series of cut-off P value of 0.05 (Supplementary Table P = 1.76 × 10–4), followed by cell morphogenesis involved in neuron differentiation  and regulation of synapse organization .We performed pathway analyses for the implicated genes and identified 19 significant pathways at multiple testing corrected 34. Except for complexity and emotional demands, the gene expression was significantly enriched in the brain for job autonomy, innovation, information demands, and physical demands  and testis (P = 8.38 × 10–4). It has been documented in the literature that the testosterone hormone secreted from testes is related to creativity, entrepreneurial intention, and behavior36. For physical demands, the expression enrichment was in the cerebellum/cerebellum hemisphere (P ≤ 3.03 × 10–5) and hypothalamus (P = 1.05 × 10–4), which are related to physical and motor regulations. The expression enrichment patterns of implicated genes may suggest the underlying biological mechanisms linking to specific brain regions.We also assessed gene expression in different human tissues based on the expression of the quantitative trait loci (e-QTL) databasends Fig. . The sper| ranged from 0.04 to 0.39; Supplementary Table rg| ranged from 0.37 to 0.92). Among them, physical demands exhibited correlations in the opposite direction with educational attainment (rg = − 0.91) and income (rg = − 0.79). Emotional demands displayed positive moderate correlations with education (rg = 0.37) and income (rg = 0.44).To dissect the genetic basis underlying the interplay among job attainment and other SES indices, we first examined the correlations between job attainment and educational attainment or income. The phenotypic correlations observed in the UK Biobank data were small or moderate . Figure P < 0.001). The direct effect was not significant after accounting for the meditating effects. Removing a direct effect from education to income did not change conclusions 38. In the first step, we calculated factor scores for cognitive, emotional, and physical requirements separately, based on the regression predictor results from the genomic EFA for job attainment. In the second step, we conducted a path analysis for these factor scores, mediating the genetic relationship between educational attainment and income. The path model fit the data well . The mediated genetic effect through cognitive job requirement score variable was estimated at 0.56 . Although there was a significant path between educational attainment and emotional or physical job requirement scores , no significant effect on income was observed. Thus, neither of these two latent requirements exhibited significant mediating effect .As a robustness check, we also conducted a two-step factor score path analysis in Fig. rg| 0.13–0.55; Fig. Next, we assessed genetic correlations between job attainment and well-being variables using summary statistics from either the large-scale GWAS or UK Biobank GWAS in European populations (see “Methods”). We found significant genetic correlations  between complexity, autonomy, innovation, and information demands with positive well-being variables such as subjective well-being, overall health rating, depression symptom, neuroticism, longevity, number of non-cancer illnesses, BMI and smoking initiation (|rg| 0.14 to 0.51). In contrast, emotional demands exhibited a significant genetic correlation with only the overall health rating variable (rg = 0.17). For all the six job attainment characteristics, no significant correlation was observed between job satisfaction and the number of cancer illnesses.Resutls for physical and emotional demands, however, showed different patterns. Physical demands exhibited significant genetic correlations with the above health outcomes (except for subjective well-being and longevity) but in the opposite direction, i.e., lower health status . We partialled out the genetic variance of intelligence, in evaluating the genetic correlations between job attainment characteristics and well-being. After partialling out intelligence, the correlations for complexity, autonomy, innovation, and information demands with a majority of well-being outcomes remained significant, but with attenuated magnitudes, such as overall health rating (rg 0.26–0.31), number of noncancer illnesses (rg − 0.23 to − 0.17), and BMI (rg − 0.15 to − 0.07). For physical demands, genetic correlations with three health outcomes remained significant: overall health rating (rg = − 0.20), number of noncancer illness (rg = 0.11), and BMI (rg = 0.15). For emotional demands, the genetic correlation with overall health rating remained significant (rg = 0.13).Because intelligence was reported to be associated with various well-being variablesBridging the O*NET job characteristics information with various genomic datasets, we conducted whole-genome analyses of six job attainment characteristics and post-GWAS analyses. The genetic link between educational attainment and income was explained to a large extent by the latent cognitive requirement of job attainment characteristics, rather than the latent emotional and physical job requirements. The job attainment characteristics were genetically associated with positive well-being variables examined in this study, which is consistent with findings in previous research on educational achievementand income. Nevertheless, findings for physical demands showed a significant opposite direction and those for emotional demands exhibited only a correlation with the overall well-being variable. After partialling out intelligence-associated genetic variance, the magnitude of genetic correlations attenuated but most remained significant. Integrating job attainment phenotypes into the genomic SES and occupation health studies enables us to expand the research beyond the crude measures of socioeconomic attainment and better understand the interplay between genetics, different SES aspects, and well-being.43. These findings corroborate with a previous study based on a national U.S. twin sample which showed sizable genetic influences on job demands, autonomy, and job complexity12. There have been GWAS studies on occupation-related phenotypes, such as entrepreneurship44 and self-employment45, but unsuccessful in discovering any novel genome-wide loci partially due to the small sample sizes of these studies. With a much larger sample size employed in our study, we were able to identify novel genetic markers associated with job attainment. Furthermore, the multidimensional measurements of job attainment adopted in our study went beyond the often used occupation categories or ranking6 to allow for more nuanced examination of occupational traits. Intriguingly, these six job attainment characteristics were genetically manifested by three latent factors of cognitive, emotional, and physical job requirements.The current study contributed to the emerging field of social and organizational genomics. Our study demonstrated that O*NET is useful to extract objective and numerical occupational information for genomic study; the SNP-heritability estimates of job attainment characteristics were significant and similar in size to those of other traits such as risk tolerance, education, and household income51. However, the stronger genetic correlations with other SES variables than those with intelligence suggest that additional mechanisms, such as personality and motivation53, may also be involved. Integrating job attainment characteristics into the genomic SES studies can help better understand the interplay between genetics and different social status aspects.Our results on pleiotropic relationships among SES variables indicate that the latent cognitive job requiement of job characteristics was the major underlying genetic basis linking education attainment and income. It has been well documented in the literature that, phenotypically, intelligence plays an important role in an individual’s educational attainment, occupational status, and income56. Its relationships with education and income were significant but much weaker compared to other job characteristics. Jobs that require high emotional demands typically do not always require high education and are not necessarily always compensated for in the labor market54. The finding that emotional demands had the lowest heritability estimation among the six job characteristics is in line with the relatively smaller heritability of emotional intelligence estimated to be 0.35–0.4556, in comparison to the estimate of heritability for intelligence at around 0.80 in later adulthood57. In the current study, we also found little overlapping genome-wide significant SNP between emotional demands and other job characteristics. The findings suggest possible unique biological pathways influencing emotional job requirements and their relationships with educational attainment and income.Emotional demands displayed distinctive patterns of findings compared to other job attainment characteristics. Among the six job characteristics, emotional demands seem to be much less related to cognitive ability, which is consistent with previous researchOn the other hand, physical demands held negative genetic correlations with other job characteristics. It also had strong negative genetic correlations with intelligence, education attainment, and income. Besides shared cognitive job requirement, there was no independent path mediated by physical demands from educational attainment and income. The physical demands measure includes items covering both manual work activities and risk or unpleasant work conditions. Our results suggest that the often-reported negative relationship between health hazards in the workplace and the well-being of workers might be more complex to explain than just treating hazards as extrinsic factors. Occupation selection may play a role in explaining genetic iinfluences on occupational hazards. Future studies may explore possible person-environmental interactions concerining occupational hazards and well-being.12, offer evidence that the relationships between job attainment characteristics and health variables are partially genetic. Such findings challenge the dominant assumption in research on job design and occupational health that the relationships between job/occupational status and well-being are primarily environmental in nature. They suggest that selection  may also play a role. A possible practical implication pertains to occupational selection. When people select their occupations based on their interests and capabilities , they should be also aware of the potential health and well-being implications related to such choices made by themselves. From a public health perspective, when the society sorts its workforce into different occupations, it should also provide sufficient protective resources and related policies to prevent undesirable health and well-being consequences brought about by job attainment characteristics.Genetic correlations between job characteristics and well-being revealed a consistent pattern: job complexity, control, information processing and low physical demands weregenetically associated with positive health outcomes; emotional demands had genetically less salient relationships with health indices compared to other five job attainment characteristics. Such findings, consistent with the previous twins’ studyg > 0.76). From phenotypic analysis, although it is documented that males and females are different in their occupational preferences58 and their occupational differences partially explain their income gaps59. Our genetic correlation analyses provide limited evidence for substantial sex-differentiated genetic architecture within traits. However, at the SNP level, the heterogeneity of genetic effects emerged at several loci showing sex-specific effects. In particular, more than half of the index SNPs centered on physical demands, exhibiting stronger effects in men than in women. Interestingly, one of the strongest signals for men is at locus harbouring genes UBE2K and SMIM14, which were reported to be associated with sex hormone-binding globulin levels60. Sex-varying effects for physical demands could also be related to sex-specific body composition or metabolic levels61. The underlying mechanisms for these loci and these sex differences remain to be explored.Within each job attainment characteristic, the genetic correlations between sex were moderate to high , mid-career stage (focusing on establishment and stabilization), and late career stage (focusing on maintenance)65. Nevertheless, a substantial number of pairwise genetic correlations remained significant after controlling the genetic effects of intelligence, which indicates that job attainment features are genetically associated with health outcomes through mechanisms other than intelligence. It is theorized and empirically examined that demand and control, effort-reward imbalance, and organizational justice that are embedded in the work contexts can explain the relationship between work stress and health outcomes66. Future genomic studies can examine, after partialling out the effect of intelligence, whether any unique biological mechanisms are underneath the above mechanisms.Genetic associations between job attainment and well-being were attenuated when partialling out intelligence-associated genetic variance, which provided additional evidence to support the relationship between intelligence and well-being at the genomic level67. Information on occupational transitions and career history can be incorporated into the genomic investigation to bring deeper insights on dynamics of work and occupation.Future research should also examine genetic influences and correlations of job attainment from a dynamic perspective. Throughout the life course, many people will experience occupational transitions, suggesting dynamic career mobility patterns68. Furthermore, our analyses only included those with European ancestry. The above restrictions suggest it should be cautious to generate findings to the general population of different age groups or with different ethnical backgrounds.Our study demonstrates that the UK Biobank dataset is a valuable source for occupation-related explorations. However, Participants of the UK Biobank volunteered to join in the study and the sample was not a national representative one. By design, only senior citizens were included. UK Biobank participants tended to be more affluent, better educated, and have better health conditions than the general populationOur findings should not be interpreted to advocate genetic determinism. Genetic variants cannot magically affect job attainments, income, or well-being. Influences of the genetic lottery are often carried out through various processes of selection . Further more, genetic influenes are mediated and are also often moderated by environmental factors. We encourage future research to explore such more nuanced processes and pathways during which environmental factors may moderate or mediate genetic influences on job attainment characteristics.In conclusion, through whole-genome analyses, the current study adds to emerging evidence of the underlying shared and distinctive genetic architecture for the six major job attainment characteristics, significant genetic correlations with well-being, as well as underlying genetic links with other SES variables. Integrating well-defined job attainment characteristics into SES and occupational genomic studies shed light on a more complete understanding of the genetic basis for various SES variables, and their interplay with well-being.70. In the UK Biobank data, participants’ jobs were coded using the four-digit UK Standard Occupational Code (SOC) 2000 (Field ID 132) based on detailed job information  collected during participants’ baseline visits to assessment centers for the discovery sample. We built a procedure to link the UK SOC 2000 to the U.S. SOC 2000, which allowed us to derive occupational characteristic information from the U.S. Department of Labor’s Occupational Information Network (O*NET). O*NET is based on the U.S. SOC system and includes hundreds of job characteristic measures for over 1100 occupations, sufficiently representing the national labor force in the United States, and has been used in many other countries via similar occupational classification systems72. First, we converted UK SOC 2000 codes into U.S. SOC/O*NET job titles using Computer Assisted Structured Coding Tool (CASCOT)73. Second, we manually checked and matched occupations in these two systems based on detailed job descriptions , based on the International Labor Organization’s International Standard Classification of Occupations (ISCO) to ensure accuracy and reliability of the crosswalk in the first step. For the 502,538 participants, we were able to match O*NET job titles for 274,223 individuals. The remaining 192,010 individuals who did not provide SOC codes and 36,305 who had SOC 2000 codes but no sufficient information to match the O*NET job titles were excluded.UK Biobank cohort is a population-based cohort study in the United Kingdom, which involves above 500,000 participants aged 40 years or older during their recruitment between 2006 to 201016. Sample items are: “What level of oral expression is needed to perform your current job?”, “What level of complex problem solving is needed to perform your current job?” and “What level of economics and accounting knowledge is needed to perform your current job?”. All the items used a 7-point scale . The Cronbach’s α was 0.98.The study involved six job attainment phenotypes, including complexity, autonomy, innovation, and three types of job demands . The Cronbach’s α was 0.90. Based on the prior study on O*NET creativity requirements75, we assessed innovation with 1 item: “How important is innovation to the performance of your current job?” . This item captures the definitions of creativity: the production of novel and useful ideas76.Autonomy was assessed with the same approach used previously14. We assessed information demands with 17 items. A sample item is “What level of processing information is needed to perform your current job?”. Five items were used to measure emotional demands, with a sample item as “How often is dealing with unpleasant, angry, or discourteous people a part of your current job?”. Physical demands were assessed with 12 items. A sample item is “In your current job, how often are you exposed to sounds and noise levels that are distracting and uncomfortable?”. All items used a seven-point scale indicating the levels of the demands. The Cronbach’s α were 0.96, 0.80, and 0.96 for the 3 sub-scales respectively.Job demands were measured by adapted scales69 (Supplementary Note). Our analysis included participants of European ancestry. Variants with MAF < 0.1% and with IMPUTE info < 0.3 were removed. A total of 19,400,443 imputed or genotyped variants in 408,344 individuals passed the QC procedure. We then matched job characteristic phenotypes with genotype data, yielding 219,483 individuals in the GWAS analyses.We used imputation genotypes released by UK Biobank in March 2018, which includes the full set of genotypes imputed on the Haplotype Reference Consortium (HRC) and UK 10 K haplotype resource. The quality control and imputation were done by UK Biobank and have been described elsewhere77, a program specifically developed to analyze large-scale UK Biobank in a fast and efficient manner. The association analysis for each phenotype was adjusted for age, sex, genotyping array, and top 20 principal components.We assumed an additive genetic model where the dosage of each variant was a continuous variable ranging from 0 to 2 for the effect allele. For each study, a mixed linear model accounting for the family structure was conducted to determine the association between variants and job attainment phenotypes represented as a quantitative trait. The analysis was conducted with the software BOLT-LMM v2.3.2 (see URLs)P < 5 × 10–8, independent from other variants at each locus (r2 < 0.01). A locus was defined by an index SNP with the region flanking 500 kb on both sides. The LD information was obtained from 10,000 unrelated individuals of European ancestry (UK Biobank project ID 16406). We further performed a conditional analysis to identify additional independent variants at each locus by adjusting for the lead variant using GCTA 1.92.4beta (see URLs)78. There were no additional loci further identified. For genome-wide significance for 6 phenotypes in the study, we applied the Bonferroni correction to control the family-wise error rate, i.e., a cutoff of P value of 5 × 10–8/6 = 8.33 × 10–9.Independent significant variants and their surrounding genomic loci were identified using LD-clumping in FUMA (see URLs). For single-trait GWAS, the lead variant is the one with the smallest association at the threshold of 79. The regional plot was drawn for each identified locus using LocusZoom (see URLs) and used the LD estimates from the 1000 Genomes European samples.The coordinates and variant identifiers were reported on the NCBI B37 (hg19) genome build. The functional annotation and gene mapping were performed using ANNOVAR (v.2018Apr16), including types of intronic, exonic, intergenic, 5′-UTR, and 3′-UTR, etc.In addition, we also conducted GWAS analyses by gender for the six job characteristics with 110,794 female and 108,689 male participants who are Caucasians (Field ID 22006). We used the linear mixed model to account for the genetic relatedness between individuals as implemented by BOLT-LMM and adjusted on up to 20 principal components for population stratification in the Caucasian population. Age and genotype measurement batch were also included as covariates.P values on Z-score were reported to compare genetic effects between the two age groups.For those top variants identified, we assessed genetic effects across age subgroups. We performed association analyses for individuals at 55 years old or below , and those over 55 , separately. For subgroup analysis, we applied the linear mixed model adjusted for age , sex, genotype measurement batches, and 20 principal components using BOLT-LMM. Two-sided 80  in a UK Biobank follow-up cohort of European participants  and the U.S. Add Health Wave V study of European individuals2 ≥ 0.01) passing a series of P value thresholds from 1 × 10–5 to 1. The optimal model fitting with the most predictive empirical P value was evaluated using the R2 coefficients81. For each P value threshold, two multivariate linear regression models were constructed. One included PGS and adjusted for age, gender, genotyping array, and top principal components as covariates; another only included covariates. The difference between the R2 coefficients from the two regression models represents the proportion of phenotypic variation explained by the PGS.PGS evaluates the cumulative effects of thousands of genetic variants identified from GWAS, including many with small effects. We generated PGSs for the 6 job attainment characteristics in both UK Biobank follow-up cohorts and Add Health using PRsice 2 (see URLs). PGS was a summation of the number of risk alleles weighted by their effect size estimated from the discovery of UK Biobank GWAS data. Genotype data of the same testing samples were used as a reference panel for LD calculation. PRSice 2 tested each PGS calculated from the independent variants . We used “g:GOST” function to perform pathway analysis on identified associated genes. Pre-specified pathways include Gene Ontology, pathways from KEGG, Reactome, WikiPathways, and protein complexes from CORUM34 (see URLs). Gene expression values  were normalized by log2(RPKM + 1) transformation per tissue type. Differently expressed genes were pre-calculated for the average expression of genes compared to others in each tissue type using a two-sided t-test on GTEx RNA-seq data. Input genes from the gene-set analysis were tested against each of the differently expressed gene sets using the hypergeometric test. Gene enrichment analysis was performed in each tissue type separately. Significant enrichment was claimed at P value < 0.05/54 = 9.26 × 10–4.We tested for expression enrichment from the gene-set analysis in 54 tissue types based on GTEx RNA-seq data  using FUMAh225. Of 19,400,443 SNVs from GWAS summary-level data for job attainment traits, we included SNPs presented in the HapMap3 European panel, with the exclusion of the major histocompatibility complex (MHC) region on chromosome 6. SNPs with INFO ≤ 0.9 and MAF ≤ 0.01 were further removed, resulting in 1,174,163 SNPs for the LDSC regression analyses. We computed genetic correlation coefficients between each pair of job attainment traits based on GWAS summary statistics, using the bivariate LDSC method, by regressing the product of testing statistics (z statistics) from each phenotype against the LD scores30.We applied the univariate LDSC method using the software LDSC v1.0.1 (see URLs) with GWAS summary statistics to estimate SNP-rg) between sex, and tested sex differences in genetic correlations. To identify the genetic variants showing the differences in genetic effects by sex for each trait, we computed z-scores from sex-stratified GWAS regression coefficients and identified genetic variants with sex-differentiated effects. Z-score statistics= Using summary statistics of GWAS conducted for each phenotype stratified by sex, we tested whether the genetic correlations were significantly different than 1 between sex. Within each trait, we used LDSC bivariate regression to estimate genetic correlations 37. The genetic covariance structure was generated using LDSC extended method. Cumulative genetic variance explained by factors was used to determine the number of clusters and standardized factor loading of each trait for a factor. The R package “GenomicSEM” was used in the analysis (see URLs).We performed genetic exploratory factor analyses (EFA)83, overall health rating84, job satisfaction (UK Biobank data field 4537), depressive symptom83, neuroticism85, longevity86, number of cancer illnesses (UK Biobank data field 134), number of noncancer illness (UK Biobank data field 135), smoking initiation87, BMI88, and intelligence29. The bivariate LDscore regression was applied for genetic correlation calculation30. Note that for the illustration purpose, in our results, the opposite scores of the originally subjective well-being and overall health rating were displayed so the higher score indicates a person has a high status of subjective well-being and overall health status. We included the SNPs present in the HapMap3 European panel, with the exclusion of the major histocompatibility complex (MHC) region on chromosome 6. SNPs with INFO ≤ 0.9 and MAF ≤ 0.01 were further removed. The bivariate LDSC regression was applied for genetic correlation between each pair of job attainment and other traits30. For each job attainment trait, Benjamin–Hochberg FDR89 was used to correct for multiple testing for the genetic correlations analysis.We assessed genetic correlations between the job attainment characteristics and 11 well-being variables and 2 SES indices  using summary statistics either from UK Biobank data or other GWAS of European ancestry. Briefly, we included subjective well-being37. For each pair of job attainment and another trait, we fit the Genomic SEM model including three traits: job attainment trait (X), well-being or SES-related phenotype (Y), and intelligence (Z). In the path diagram, there was a bidirectional arrow between two traits of X and Y, and directional arrows from Z to X and Z to Y. The genetic effect of Z was regressed out from the variance of X and Y affecting the heritability and genetic correlation. The genetic covariance matrix of X, Y, and Z was produced by LDSC method implemented in Genomic SEM. The same variant filtering procedure for calculation correlation before partialling out the genetic variance of intelligence was applied in the Genomic SEM analysis. The process was repeated for each well-being outcome.To estimate the genetic correlations after partialling out the genetic variance of intelligence, we used the Genomic SEM37. GWAS summary statistics were used for education attainment8 and household income90. We explored several SEM models by including (1) one latent factor underlying job attainment, representing overall job requirement, (2) adding a direct effect from the education to income, (3) two-factor models by adding the second factor of emotional job requirement or physical job requirement separately. As the model did not converge by including all six job characteristics simultaneously, we excluded complexity since its composite nature and high correlations with other traits. Model fit was evaluated using Akaike Information Criteria (AIC), Comparative Fit Index (CFI) and the standardized root mean square residual (SRMR).We conducted Genomic SEM analyses examining the joint genetic paths among educational attainment, job attainment, and income91 for the indirect effects from educational attainment to income through job attainment (see URLs).We used the Sobel testTo confirm the robustness of the results from genomic SEM, we also performed a two-step path analysis to compute composite scores for three latent factor scores based on the regression predictors estimated from the genomic EFA modeling for 6 job attainment characteristics Table . The jobS). We estimated the regression coefficients for the paths between educational attainment, latent factor scores, and income. Model parameters were estimated using the Diagonally Weighted Least Square (DWLS) method implemented in Genomic SEM.We ran multivariable LD-Score regression to obtain the genetic covariance matrix (S) and corresponding sampling covariance matrix (VSupplementary Information.


Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two‐step analysis. The genetic mechanism that leads to insensitivity of a phenotype to genetic and environmental perturbations is known as “canalization”  of a genotype. In other words, genotypes that produce more stable phenotypes have lower Ve, and a decrease of Ve due to selection indicates canalization  on the trait value of the individual itself. However, most individuals are not solitary but rather social organisms, and with social interactions, such as competition and cooperation, the trait value of an individual may be influenced not only by the individuals’ own genes but also by the genes of its social partner. This heritable effect of a social partner on the trait values of the focal individual is known as an indirect genetic effect  leads to inherited variability of trait values, using the model of Marjanovic et al. . Subsequ22.1In this section, we summarize the quantitative genetic model of Marjanovic et al.  that intb,Pt,i is the body weight of focal individual i at time point t, Pt‐1,i is the body weight of i at the previous time point, μGR is the mean growth rate of the population, AGR,i is a (direct) breeding value for the growth rate of individual i, Ep,GR,i and Et,GR,i are permanent and temporary non‐heritable  effects of individual i, and bij is a regression coefficient.In this model, we consider groups of two individuals. Each individual is both a focal individual in the model for its own phenotype, and a social partner in the model for the phenotype of its group mate. In aquaculture, the growth of individuals is affected by the difference in body weight between interacting individuals, with higher body weight giving a competitive advantage to an individual in terms of growth , representing “genetic resistance to competition,” and an indirect genetic effect of its social partner, representing the “genetic cooperative effect” . Hence, the model allows for variation among individuals in sensitivity to competition, so that some individuals may suffer less from the presence of a large social partner than others. Similarly, the model allows for variation among individuals in competitive effect. Some individuals may be large at the expense of their group mate, whereas other large individuals may not suppress the growth of their social partner. Thus, for focal individual i with social partner j, the regression coefficient bij is given byb¯ represents the average regression coefficient, which is a population parameter that is negative under competition and positive under cooperation. The AD,i and ED,i are the direct genetic and environmental effect of individual i on bij, while AI,j and EI,j are the indirect genetic and environmental effect of individual j on bij. As common in quantitative genetics, A and E are defined relative to a mean of zero. A negative value of AD indicates that the individual is sensitive to competition , while an individual with positive AD is resistant to competition. Similarly, an individual with negative AI is competitive, in the sense that it suppresses the body weight of its partner by a relatively large amount, while an individual with positive AI is cooperative. Note that b is non‐symmetric, that is, bij≠bji, as individuals may differ in their breeding values for b. In other words, an individual that is strongly affected by its social partner, does not necessarily also have a strong effect on its social partner.In this model, AGR, a direct breeding value for b (AD) representing genetic resistance to competition, and an indirect breeding value for bAI representing a genetic cooperative effect.Therefore, in the total model . Thus, to create records on the variability of body weight, each dam produced 100 offspring, resulting in 10,000 offspring per sire, and a total of 1 million offspring.We first simulated a base population of 100 sires and 10,000 dams, all unrelated. Each animal in the base population was assigned a breeding value for growth rate, and a direct and indirect breeding value for b of the offspring were simulated as the average breeding value of the sire and dam, plus a Mendelian sampling term drawn from N000,12σAGR2000σAD2000σAI2. In addition, each offspring was assigned a permanent and temporary environmental effect on body weight, and a direct and indirect environmental effect on b. These were drawn from N0000,σEp,GR20000σEt,GR20000σED20000σEI2. In the default scenario, the genetic and environmental covariances were all set to 0.The breeding values for growth rate and direct and indirect breeding values for Groups of two members were created by randomly assigning a social partner to each offspring, which resulted in 50,000 groups for the analysis of trait levels, and 500,000 groups for the analysis of trait variability. Subsequently, phenotypes for 10‐time points were obtained for all individuals by using Equations 2.2.2μGR) was also 10 g. The genetic standard deviation of growth rate (σAGR) was set to 1 g. . Phenotypic variance was calculated as σP2 = σAGR2/h2 and was equal to 2 g2, permanent environmental effect on growth  as 0.2 σP2 = 0.4 g2 and temporary environmental effect  as 0.3 σP2 = 0.6 g2 , 0 , or 0.05 (cooperation). The total standard deviation of b¯ was set to 60% of 0.05; σb=0.03. Therefore, standard deviations of genetic and environmental components of b had to satisfy σAD2+σAI2+σED2+σEI2=0.03. All standard deviations were assumed equal; hence, each of them had a value of 0.015  a direct sire model, and (ii) an indirect sire model. For the trait, these were (iii) a direct sire–dam model, and (iv) an indirect sire–dam model. We used sire models and data grouped by family for inherited variability, because this is a simple and robust approach to estimate genetic parameters and genetic effects for 2.3.1(sD). The model was:yv,D is the vector of log‐transformed within‐family variance of body weight, µ is the overall mean, sD is a vector of direct random genetic effects of sires, with sD ~ N, where σSD2 is the direct sire variance, ZDs is an incidence matrix linking observations to sires, and e is the vector of random residuals, with e ~ N.As a measure of the direct component of inherited variability, we used the log‐transformed within‐family variance of body weight. Log transformed within‐family variance of one full‐sib family was treated as a trait of the sire, so that each sire had 100 observations of within‐family variance, each based on 100 offspring per sire–dam combination. This model corresponds to an ordinary model for inherited variability , we used the log‐transformed variance of body weight of the group mates of full‐sib families descending from the sire. Thus, each sire had 100 records, which were the log‐transformed variance of body weight of the group mates of each of the 100 families produced by a sire. The model was:yv,I is the vector of log‐transformed variance of body weight of the group mates of the full‐sib families descending from the sire, µ is the overall mean, sI is the vector of indirect random genetic effects of a sire, with (sI) ~ N, where σsI2 is the indirect sire variance for variability, ZIs is an incidence matrix linking observations to sires, and e is the vector of random residuals, with (e) ~ N.Indirect genetic effects are expressed in the phenotypes of social partners. Therefore, to estimate indirect random genetic effects of sires for variability 2.3.3yt,D is the vector of individual body weight records of offspring, µ is the overall mean, pD is the vector of direct random genetic effects of sires and dams (“parents”), with pD ~ N, where σpD2 is the direct sire–dam variance, ZDp is an incidence matrix linking observations to parents, and has a “1” in the column for the sire and in the column for the dam of the offspring producing the record, and e is the vector of random residuals, with e ~ N. This model gives estimates of half of the direct breeding values of a parent for body weight (pD).Here, we use an ordinary sire–dam model, which assumes equal genetic variance for sires and dams. The model was as follows:2.3.4yt,I is the vector of individual body weight records of individuals, µ is the overall mean, pI is the vector of indirect random genetic effects of the parents of the group mate of the focal individual, with pI ~ N, where σpI2 is the indirect sire–dam variance, ZIp is an incidence matrix with “1” in the column for the sire and in the column for the dam of the group mate of the focal individual, and e is the vector of random residuals, with e ~ N.In this model, we link the observation on an individual to the sire and dam of its group mate. The model was:33.1AGR, Tables Both direct and indirect estimated sire effects for variability showed near‐zero correlations with simulated breeding values of sire for growth  under competition, cooperation, and for neutral b (Table b) show lower variability of body weight. Correlations between estimated direct sire effects and simulated indirect breeding values for b (AIb), on the other hand, were near zero, under competition, cooperation, and for neutral b. These results indicate that cooperative effects of sires (AIb) have negligible effect on the phenotypic variation among their offspring. In conclusion, these results suggest that current  models of inherited variability capture mostly the direct genetic effects (ADb) of competition, but not the indirect effect (AIb). In other words, they capture the sensitivity of individuals to competition, but not the competitive effects of individuals on the phenotypes of their group mates.The estimated direct sire effects on variability showed strong negative correlations with simulated direct breeding values for  b Table . Therefob , or lower indirect genetic variance in b (σAI), correlations between estimated direct sire effects and simulated direct breeding values for b (ADb) were slightly closer to −1. The opposite was true for lower direct genetic variance in b (σAD) and higher indirect genetic variance in b (σAI). When the direct genetic variance in b was small, or when the indirect genetic variance in b was large, the direct model for inherited variability captured more indirect genetic effects, resulting in higher negative correlations between estimated direct sire effects and simulated indirect breeding values for b (AIb), Table With a higher direct genetic variance in 3.1.2b (AIb) were strongly negative, in competition, cooperation, and neutral scenarios  were small and negative under competition, and close to zero under cooperation and for neutral b. Thus, indirect models of inherited variability capture mostly indirect genetic effects of competition, but not the direct effects (ADb). In other words, they capture the competitive effects of individuals on the phenotypes of their group mates, but not the sensitivity of individuals to competition.Correlations between estimated indirect sire effects on variability and simulated indirect breeding values for os Table . This reb , the correlation between estimated indirect genetic effects of a sire and indirect breeding values for b was closer to −1. When σAI was low or when direct genetic variance in b (σAD) was high, correlations between estimated indirect genetic effects of a sire and simulated direct breeding values for b slightly increased.With a higher indirect genetic variance in 3.2b were near 0 (results not shown). Trait models, therefore, do not capture genetic effects of competition generated by the model in Equations Correlations between estimated sire and dam effects for growth, from both direct and indirect sire–dam models for trait values and simulated direct and indirect breeding values for 3.2.1Estimated direct sire and dam effects for growth showed a strong positive correlation with simulated direct breeding values (~0.83) for all scenarios (results not shown). Correlations were lower than 1 because dam effects were based on only 10 observations; Correlations were near 1 when considering sires only (results not shown).3.2.2b¯. Thus, individuals with high genetic potential for growth reduce the growth of their group mates under competition but increase the growth of their group mates under cooperation. Changes in values of σAGR, σAI, and σAD had only a minor effect on the estimated correlations.Estimated indirect sire and dam effects showed a moderately negative correlation (−0.33) with simulated breeding values for growth under competition, but a moderate and positive correlation (0.26) under cooperation. Hence, the sign of the correlation corresponded to the sign of 3.3AIb and AGR increased, when ADb had a non‐zero correlation with AIb and AGR . Results are in Tables GR Table . SimilarGR Table , and in 4b. Similarly, an indirect model of inherited variability captures the indirect genetic effects of competition. Models for trait levels, however, capture only a small part of the genetic effects of competition.We investigated whether current statistical models for inherited variability and for trait values capture direct and indirect genetic effects of competition on variability. Our results show that the ordinary direct model of inherited variability almost entirely captures the direct genetic effect of competition on variability, as illustrated by large correlations between estimated genetic effects and simulated direct breeding values for 4.1b comprises both a direct and an indirect genetic effect. Using simulations, we demonstrated that IGEs and variability can co‐evolve, because the regression coefficient can respond to selection, similar to in trait‐based IGE models  is not exploited. In addition, the presence of IGEs on b may cause a response in variability to divergence from its expectation, particularly when they are correlated to direct genetic effects on b . These techniques can provide large‐scale time‐series data on individual trait values  and information on social interactions between individuals. The combination of such technologies with new models for interactions among individuals, such as Equations For estimation of direct and indirect breeding values for 5Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, while an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two‐step analysis or a different data setup involving larger groups.The authors declare that they have no competing interests.Table S1–S4Click here for additional data file.


Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration , methane production , methane yield , methane intensity , residual methane concentration (RMeC), residual methane production , and two definitions of residual feed intake with or without including body weight change . The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used . Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.In the last decade, several countries have included feed efficiency  in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH Over the past decade, the scientific community has investigated different paths to reduce CH4 emissions, through different scientific disciplines, such as animal nutrition, physiology, management and genetics , is becoming more popular. These methods, though, measure CH4 as concentration  not MeP. For this reason, some authors have used methane concentration as trait , or input like methane yield (MeY = g CH4/kg feed). However, selecting for a ratio trait present some statistical disadvantages like strong correlations with the denominator, in this case represented by economically important trait(s) already present in the breeding goal. Additionally it presents difficulty of interpretation of the selection response due to antagonism between the response in the numerator and the denominator  have included feed efficiency in their breeding goal through some index called saved feed or feed saved . Residua4 traits , including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the economic impact of reducing methane emissions in Holstein dairy cattle in Denmark as an example.The objectives of this study were to 1) estimate genetic parameters for six CH4 and CO2 on 650 Holstein cows recorded between 2013 and 2020 at the Danish Cattle Research Center  were available. Data have been partially described previously by Zetouni et al.  was measured by the non-dispersive infrared CH4 sensor , and in parallel, CO2 was measured using the same technique  installed in each of the three automatic milking stations (AMS). Equipment details, technical specification as sensor calibration and management of the raw data from thresholds for ambient background, and a cow head-lifting algorithm are reported in Measurements of CHi et al. , Difford4 and CO2 records were calculated to match the weekly records of ECM and BW available to calculate MeP. Data were filtered to only include the weekly averages, where a maximum of 3 days was allowed to be missing within a week, and individual cows required a minimum of three weekly measurements to be retained for further analysis. After editing, 26,664 weekly MeC records from 647 Danish Holstein cows and 19,123 MeP records from 575 from Holstein cows were analyzed. Natural logarithm (ln) transformation was applied, as MeC was not normally distributed, and this was multiplied by 100 to avoid problems with the scale of the other traits. Methane production was calculated as follows using the formula of  was defined as MeP divided by DMI, and methane intensity (MeI) was calculated using MeP divided by ECM. Residual methane concentration (RMeC) was the residual of the partial regression of MeC on ECM and MBW, whereas RMeP was the residual of the partial regression of MeP on ECM and MBW along with fixed effects described in the model (5). Furthermore, residual feed intake 1 (RFI1) was the residual of the partial regression of DMI on MBW and ECM , variance components were estimated using the AI-REML algorithm with the DMU software . GenetiEYS is the fixed effect ith for experiment-year-season (115 classes); LACTWEEK is the fixed effect jth for a week of lactation (44 classes); PAR is the fixed effect for the kth parity number ; and ACC is the age of cow at calving in months as covariate. Random effects are as follows: a is the additive genetic effect lth distributed as N  distributed as N . Within each index, there were three possible scenarios reflecting past, current, and future situations. Given the lack of information on the correlation between functional traits included in the NTM  and methane production, this exercise only focused on ECM, BW and RFI. Furthermore, all scenarios are assuming zero correlations with health traits based on Zetouni et al.  to a 20-year global warming potential period where 1 kg CH4 = 84 CO2e. For MeI, the economic values were calculated as the trait definition, g MeP/kg of ECM being equal to -0.0083 for scenario 2A (−0.005/0.6) and −0.0283 (−0.017/0.6) for scenario 2B. The economic values for MeP and RMeP are given in euro per g/d, whereas the economic values for MeI are given in euro per g MeP/kg ECM; and ECM and RFI are given in euro per kg/d. All scenarios set economic values for MBW to zero. Genetic variances, correlations and heritabilities obtained from this study were used in the calculation of the expected responses of ECM and RFI and the correlated responses for MBW and MeP or RMeP. Furthermore, reliabilities of the EBVs were assumed at 0.81 (average reliability for ECM).Given that currently RFI is part of the saved feed index  and it hi et al.   to a 4/kg ECM and for MeY, it is 15.4 g CH4/kg DMI. These averages are similar to the values previously reported by Descriptive statistics for all traits  are presented in Estimated genetic, permanent environmental, residual variances, heritabilities, and repeatabilities for all traits are presented in g) and phenotypic correlations (rp) between the six methane traits  are presented in g between MeP (the reference trait) with the other methane traits ranged from 0.48 ± 0.16 (MeI) to 0.82 ± 0.07 (RMeP). Furthermore, RMeP was highly (above 0.8) genetic and phenotypically correlated with all the traits except for MeC; this could represent an advantage when this trait is added to the breeding goal given that it could represent all the other methane phenotypes. For example, as for farmers and nutritionists, MeI and MeY are important, and the correlations between RMeP and MeI and MeY are 0.85 and 0.88, respectively, and RMeP could be used as a proxy for them.Estimated genetic (rg between RFI2 (including ∆BW) ranged from 0.05 ± 0.20 (MeI) to 0.76 ± 0.09 (MeP). These positive genetic correlations are favorable when selecting for RFI, given that the goal is to reduce RFI; this will also reduce methane emissions (in different magnitudes depending on the trait). However, for some of the traits  due to the large standard errors, these genetic correlations were not different from zero. The highest correlations with RFI (1 and 2) were found with MeP and RMeP. Nevertheless, for MeP, it would be difficult to increase milk and decrease methane at the same time given the high genetic correlation between MeP and ECM (0.79). Thus, RMeP could be suggested as a trait to include in the breeding goal given its low to zero correlation with ECM (0.12) and BW (0.06), meaning that this trait is almost independent of these traits and we could decrease methane without compromising milk production or the weight of cows. However, this suggestion is based on our results, including high and positive genetic correlations between RMeP, MeP, and RFI; further analyses with more data would be recommended to confirm these results.Estimated genetic and phenotypic correlations between methane traits and efficiency traits are presented in 4 breath concentration (MeC) (4 (and CO2), ECM and BW are needed  . Howevere needed , leadinge needed . This ine needed  proposede needed  acknowle4/kg ECM) decreased in all scenarios. The scenario zero (SC0) showed a scenario where the main trait to select was ECM. In SC0, the correlated response of MeP was an increase of 24.22 g/d, whereas for RMeP, it was 2.71 g/d; this would represent an increase of 7% with respect to the current average (337.9 g/d) for MeP. For MeI (in SC0), the correlated response was -0.41 g of CH4/kg of ECM. Scenario 1 (SC1), where RFI is included, observed a reduction in MeP of 0.5 g/d and for RMeP of 0.4 g/d. For MeI (SC1), the correlated response was -0.42 g of CH4/kg of ECM, which represents 1 g less per kilogram of milk in comparison to SC0. These reductions are per animal per generation and they are cumulative. Scenario 2, where we included an economic value for methane emissions at two different levels, reduced MeP by 1.43 g/d (SC2A) up to 4.16 g/d (SC2B) compared to SC0, whereas RMeP reduced by 1.37 g/d (SC2A) up to 3.59 g/d (SC2B). These reductions in SC2 represent CH4 reductions of 6–17% compared to SC0 independently of the trait. For MeI (SC2), the correlated response was −0.43 g of CH4/kg of ECM, which represent 2 g less per kilogram of milk in comparison to SC0. Translating these values to kg per cow per lactation, for SC2A these reductions are between 0.44 (MeP) and 0.42 kg/cow/year (RMeP), whereas for SC2B, these reductions are between 1.3 (MeP) and 1.1 kg/cow/year (RMeP). Similarly, Gonzalez-Recio et al.  and that differences observed are a reflection of the variance in DMI between animals  of methane emissions by including RFI in the breeding goal, it seems that this reduction will not be enough to reach the intended global reductions of GHG at country and at the EU level . Therefore, additional (negative) weights should be included for methane traits to accomplish a desired reduction in the GHG emission from dairy cattle in the next 10 and 30 years. For this reason, including methane through one of its traits is a potential way to accelerate this reduction on methane emissions. The outcome of this analysis shows that including either MeP or RMeP in the breeding goal would be translated as a deceleration of the increase of methane emissions compared to the actual scenario, where no methane trait is included. However, the advantage of RMeP would be that this trait is independent of the production and body weight of the animal. For this reason, the effect of including an economic value for methane has smaller effect on the genetic gain of ECM, in comparison to that of MeP. This is the primary advantage of residual traits; in this case, RMeP has been corrected by ECM and MBW, thus having low or close to zero correlations with the production traits.4 per kg of ECM produced. In this study, we also included MeI as a candidate trait to visualize the correlated response of MeI when including RFI in the breeding goal. The response was favorable (negative), meaning that selecting for efficient cows will also have a positive impact by reducing methane (−0.41 to −0.43 g) for methane emitted by the unit of production (kg of ECM). As mentioned before, given the increase in milk production per cow, fewer cows are needed to produce the same amount of milk. So, independently of the amount of CH4 produced per cow, the MeI would be diluted by the higher yield, meaning that a high producing cow will have lower MeI than a low producing cow. In the last decade, as a result of selection on highly milk producing cows, MeI (g CH4/kg ECM) has shown a constant decrease , to convert ton CH4 to ton CO2e requires a multiplication of 25  or for 84 . For MeP, this would represent 17.4 K ton CO2e or 58.6 K ton CO2e depending on the period of global warming potential used, whereas for RMeP, this would represent 15.3 K ton CO2e or 51.4 K ton CO2e. Based on the previous calculation of 1,500 DKK (or €200) per ton CO2e according to 4 reduction of 568,100 ton (22.7 K ton/year) was achieved, which represented €8.5 million/year in carbon credits  has a positive impact on reducing methane emissions independently of the trait used; nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane traits in the breeding goal shall be the preferable way to achieve the desired methane reductions in dairy cattle. Consequently, including methane in the breeding goal seems to be imperative in order to obtain the intended reduction of methane emissions in the coming years, despite the possible deceleration in the genetic gain of milk production. Additionally, if we translate the possible reductions of methane achieved by including methane in the breeding goal, this could lead to savings up to 11 million of Euros in 1 year to the dairy industry, just for Denmark.


Dioscorea rotundata) genotypes using multi-trait index-based factor analysis and ideotype design (FAI-BLUP). The experiment utilized 36 white yam genotypes laid out in a 6 × 6 triple lattice design with three replications and phenotyped for 18 agronomic and food quality traits. Findings showed significant differences among genotypes for all assessed traits. Fifteen traits had desired genetic gains, whereas stem diameter (−1.34%), and two starch property traits ((holding strength (−26.31%) and final paste viscosity (−3.33%)) had undesired selection gain. The FAI-BLUP index provided total genetic gains of 148.91% for traits desired for increase and –29.26% for those desired for decrease. Genotypes TDr08-21-2, TDr9518544, TDr9501932, TDr8902665 and Pampars were identified as top best candidate for simultaneous improvement of the measured traits in white yam breeding. The findings indicate the effectiveness of the FAI-BLUP index in identifying and selecting genotypes.Choosing superior parents with complementary trait values for hybridization and selecting variants with desired product profiles to release as a new cultivar are important breeding activities to progress genetic improvement in crops. This study assessed the genetic potential of 36 parental lines of white Guinea yam ( Dioscorea spp.) is a clonally propagated tuber crop belonging to the genus Dioscorea in the Dioscoreaceae family. It comprises >600 species, of which 11 are economically important and widely cultivated species  vector was considered as the ideotype matrix.The factor analysis (FA) was done to account for the dimensionality reduction of the data and relationship structure. This analysis was performed according to the following model:By definition based on Equation (9), the ideotype has the highest rescaled value (100) for all analyzed traits. Thus, the ideotype can be defined by a 1 × p vector I such that I = . The scores for I are also calculated according to Equation (9).n) to be similar to the jth ideotype ; The genetic values of traits were utilized for the estimation of the multi-trait index based on the FAI-BLUP index . The desThe factor analysis and ideotype-design (FAI-BLUP) index were calculated for ranking of the genotypes based on multi-trait, free from multicollinearity . A selecThe predicted genetic gain This study has provided better understanding on the genetic control and selection of the key multiple-traits in the white Guinea yam breeding program. Genotypes identified through factor analysis and ideotype-design (FAI-BLUP) index technique could be exploited as potential trait progenitors for population improvement targeted at good agronomic and end-product quality in white Guinea yam. Hybridization involving genetically diverse genotypes identified from the top 5% of superior progenies is expected to increase the frequency of favorable alleles and maximize genetic variability. The superior genotypes detected with higher genotypic values using the multi-trait selection index should be further exploited for possible commercial deployment in suitable production environments.