A) and its proxy measures based on δ13C (WUEi) or gas exchange  of genotypes from half‐sib families of Lolium perenne L. (PRG) in a simulated summer drought cycle. Principal component analysis (PCA) of trait data distinguished a group of PRG genotypes where high WUEA and dry matter yield was associated with deep rooting, leaf hydration at more negative leaf osmotic and water potential, and reduced soil moisture depletion. Plants with this trait association sustained net assimilation and postdefoliation regrowth in drought. However, WUEi, WUEASC, and WUEAET were poorly correlated with most traits of interest at p < .05. Another PCA revealed a weak association between WUEA and its proxy measures under conditions tested. Quantitative genetic parameters including high estimates of narrow‐sense heritability  of WUEA and related traits emphasized the genetic potential of the key trait combination for selecting PRG for improved drought tolerance. Research findings highlight the relative importance of WUEA and its proxy measures in the broad definition of PRG drought tolerance for breeding purposes.Water‐use efficiency (WUE) is an under‐researched but very important drought tolerance trait in forage breeding. This research estimated quantitative genetic parameters of morpho‐physiological traits linked to agronomic water‐use efficiency  at the whole‐plant level over its proxy measures for forage breeding purposes. The result was generated by key drought‐tolerant trait associations linked to WUEa and reinforced by its higher genetic potential and repeatability compared with those of surrogate measures under the conditions tested. This is the logical basis upon which WUEi, WUEASC, or WUEAET are proposed as proxy measures of WUEA  truly capture the whole‐plant level WUE signal in a PRG plant under soil drought conditions, specifically for pasture selection and breeding purposes.Drought causes an imbalance between the evapotranspiration demand of a plant and the actual soil water uptake, thus generating a hydraulic signal that leads to both synergistic and antagonistic changes in plants' physiology and metabolism  estimated quantitative genetic parameters of different WUE measures and related traits to understand their usefulness in improving PRG drought tolerance by breeding. These aims are designed to improve our understanding of the contribution of plant water relations traits to drought tolerance and to provide plant breeders with relevant information to inform decisions on selection criteria in plant breeding programs targeting improved drought tolerance.To address these knowledge gaps, the experiment reported here (i) assessed inter‐relationships among WUE, other plant water relations traits, and morpho‐physiological trait responses aligned with improved PRG drought tolerance; (ii) measured WUE22.1The main experiment included 36 half‐sib families, carrying the AR37 endophyte, sampled from an advanced generation commercial breeding pool. There were five randomly sampled genotypes from each half‐sib family, two clonal replicates of each genotype, and a further 40 plants  included as checks to adjust for spatial variation  for transplanting into pots. Pots were placed in a row‐column design with border plants in the glasshouse and time allowed for root system development, as indicated by leading roots reaching the bottom of pots before the drought treatment started  until the next defoliation 35 days later. Then, the watering interval was progressively increased, and the water volume decreased until the soil reached 55%–65% of FC MS2) as determined by weighing the pots individually on an electronic balance to 1 g precision. Water was manually added 2–4 times weekly as required to keep all the pots at a constant weight and water use of each potted plant was recorded . After 28 days at MS2, plants were defoliated again and maintained at 45%–55% FC (MS3) for another 14 days. Thus, three sets of detailed measurements of plant morpho‐physiological traits were carried out on all plants within 4‐day time windows at each of M1, M2, and M3 phases, for all the plants as described in Tables  until th as deter2.3T and SMCD, respectively), remaining fraction of soil available water (FASW), agronomic water‐use efficiency (WUEA), photosynthetic gas exchange‐based instantaneous WUE based on stomatal conductance or evapotranspiration (WUEASC and WUEAET), and , deep root dry weight at 20–50 cm soil depth (RDWD), postdefoliation regrowth score (RGS), tiller number (TN), photosynthesis (A), stomatal conductance (SC), evapotranspiration (ET), and carbon isotope discrimination  were recorded (or samples stored for later measurement) within a 4‐day time window at least twice on the full set of plants  at the end of each successive postdefoliation regrowth phase or measurement stages 1, 2, and 3  of a progressively intensifying water deficit , leaf osmotic potential (OP), predawn leaf water potential (LWP), gravimetric soil moisture content at 10–20 and 40–50 cm depths (SMC13C (‰) of leaf tissue by isotope ratio mass spectrometry (IRMS) of leaf tissue collected at MS1 and MS2 to compare and contrast four measures of WUE: WUEA, WUEAET, WUEASC, and WUEi. Genotypes for IRMS analysis of stored leaf tissue were selected on a “stratified random” basis by ranking the 36 half‐sib family means of the main experiment in ascending order of WUEA and selecting every alternate value excluding two near‐equivalents. The sample subset therefore comprised two clonal replicates of five genotypes of 16 families plus the 40 check plants (n = 200), but largely retained the statistical variance properties of the larger dataset with respect to WUEA. Stored leaf samples for IRMS were first re‐dried at 60°C for 48 h in an air‐draft oven, powdered, and then submitted for carbon isotope analysis to derive carbon isotope discrimination (Δ13C), δ13C (‰), and carbon: nitrogen ratio (CN) data. For these determinations, the samples collected at Massey University, New Zealand, were analyzed at Technical University of Munich, Germany, using an elemental analyzer  interfaced  to an isotope ratio mass spectrometer   to establish all‐inclusive key drought‐response patterns and to discern the relative importance and interrelationship of different WUE measures in PRG drought tolerance at MS2 , and correlated response to selection for the measured traits were estimated  at each consecutive measurement stage of the progressively intensifying drought cycle (Table p > .05) with an increasing trend in statistical significance of variation toward the end of the drought cycle  toward the end of the experiment, along with a corresponding declining trend in average A, SC, ET, WUEASC, and WUEAET and an increasing trend in SDW from MS1 to MS3  from MS1 to MS2. However, WUEA and gas exchange measurements were weakly correlated at all three measurement stages , RWC (PC coefficient 0.326), more negative OP (PC coefficient −0.333), enhanced gas exchange , deep rootedness , and conservation of soil moisture at deep layers or high SMCD (PC coefficient 0.304)  linked to decreased SMCT with modestly positive coefficients for SDW explaining 14% of data variation. Paradoxically, this PC had negative coefficients for gas exchange and net assimilation .To visualize the major data variation for the traits measured in the full set of PRG half‐sib families at MS3, the first three PCs explaining 79.8% of data variation  of PCA1 were selected Table . PC1 repon Table  and wereD and related physiological traits that reduce soil moisture depletion, but reduced investment in upper root dry weight as co‐occurring in plants with high scores for PC1 of PCA1.Taking trait associations of PC1 collectively as a mathematical description of the major drought‐response mechanism in this PRG population, Figure 3.3A, WUEi, WUEAET, and WUEASC were statistically significant at p < .05 (Table p > .05) at MS1 (data not shown). The highest correlation was between WUEAET−WUEASC  followed by WUEA−WUEi . WUEi was moderately correlated with SC and RWC and highly correlated with SDW (and TN), A, and FASW at p < .05, while WUEAET and/ or WUEASC were significantly but weakly correlated with FASW, RWC, SDW, and OP at p < .05  while explaining 48% of total data variation. However, in common with PC1 of PCA1, PC1 of PCA2 also associated increased SDW with enhanced WUE but with a negative sign for FASW . PC3 identified counterintuitive negative relationships between WUEA and its proxy measures. With only 7% and 6%, respectively, of the total data variation explained, PC4 represented two noticeably associated traits among others  with positive WUEi, WUEASC, and WUEAET and negative WUEA. PC5 identified one trait  largely independent from all WUE measures  from the total of 17 PCs of all the traits measured at MS2 were selected Table . PC1 of 3.4p > .05) for most traits in early drought (MS1). For the selected subset of half‐sib families, most plant traits exhibited higher genetic among‐half‐sib family (σf2) and within‐half‐sib family (σf/s2) variances (excluding SC) and narrow‐sense heritability (hn2) estimates at MS3 than at MS2 . By contrast, hn2 estimates for WUEAET and WUEASC were statistically nonsignificant at both MS2 and MS3 . The coefficient of variation observed for the heritable traits was within an acceptable range for plant breeding purposes  at both MS2 and MS3 (Tables Table c) estimates of most traits were higher at MS3 than at MS2 (Table c estimates of among‐family‐selection (AFS) and among‐and‐within‐family‐selection (AWFS) selection at 30% of selection pressure were for RDWD (9%–18%) followed by RGS and WUEA (4%–8%) among all traits of interest. At MS2, ΔGc estimates of AFS and AWFS for WUEi ranged from 2% to 4%  showed more significant ΔGc estimates at MS1 than at MS2 and MS3 (data not shown).Predicted genetic gain  at both MS2 and MS3 (data not shown). However, there were a few exceptions for WUEA‐OP, WUEA‐RDWD, WUEA‐SC, OP‐RDWD, and OP‐SC trait pairs, where their rg coefficients ranged from 0.3 to 0.5 in both MS2 and MS3 . Correlated response to selection estimates of highly genetically correlated traits was generally similar  or mostly lower  compared with the ΔGc of single‐trait selection estimated for each key trait (data not shown). ΔGc by AFS and AWFS of WUEi and WUEA ranged from 2% to 4% and from 4% to 8%, respectively, (see above) while correlated response to selection estimate of WUEA‐WUEi ranged from 2% to 4% at the 30% selection pressure (p < .05). However, the correlated response to selection estimate of the OP‐RDWD trait pair (10%–20%) was larger than the ΔGc estimates of OP (3%–6%) and RDWD , is one of the most common drivers of plant dehydration avoidance and is a pathway for maintaining both the production –OP100 (MS2) and PC1 scores of PCA1 was 0.53; data not shown) and reduced soil moisture depletion (positive SMCD) or regulated plant water use (high WUEA or low WU). These trait expressions together seemed to constitute a constructive dehydration avoidance mechanism (hereafter referred as WUEA‐OP mechanism) of PRG. This trait expression is however inconsistent with the EUW concept advocated by Blum (D and low RDWT linked to well‐regulated SMC extraction) over deep and dense rooting features and indicate balanced plant dry matter partitioning features in the WUEA‐OP trait mechanism of drought‐tolerant PRG  or shallow‐rooting PRG . Zhan et al.  is linked to low SDW and TN  and restricted net assimilation . Interestingly, the reduced SDW scenario linked to low WUE results in increased retention of available soil water (coefficient for FASW was +0.313), and OP was not involved in this trait association. This dehydration tolerance scenario is consistent with literature where reduced SDW under water deficit was associated with reduced soil moisture depletion and vice versa  measurements of photosynthetic gas exchange derived from data collected at one or a few time points may not completely represent the mean growth conditions over the entire drought cycle that determine WUE for harvested biomass  and had near‐zero influence on gas exchange parameters or WUEAET and WUEASC , although there was a strong association between more negative OP and increased RDWD. This is counterintuitive but discounts the possibility that the physiological mechanism of the WUEA‐OP trait association might be a conserving effect of more negative OP on A and ET.With respect to agreement between WUE Condon, . PCs 2, A and WUEi) and the instantaneous measures of WUEASC and WUEAET  with WUEASC and WUEAET numerically high (indicating low WUE) when ET and SC were high . However, before using any of the trait/s of interest for selection and pyramiding of correlated traits in drought‐tolerant PRG genotypes, information on the key quantitative genetics on both individual  and correlated  trait responses is required. In selection programs, heritability is the primary parameter that numerically quantifies whether the progress from selection for a given trait (s) will be straightforward or challenging  describes the ratio of additive genetic variance, which allows permanent and continued response to selection, to phenotypic variance of measured traits  and 90% genetic potential at MS2 and MS3, respectively  is cheaper and/or easier to measure in larger populations and is under higher selection pressure than that of the primary trait  under the conditions tested. Therefore, from both physiological and quantitative genetics perspectives, our results suggest that it is advantageous to select drought‐tolerant PRG plants directly based on WUEA  than indirectly based on WUEi. Besides, the correlated response to selection estimates of highly genetically correlated and heritable key traits with WUEA  was lower or similar to ΔGc of each secondary trait, indicating that WUEA should be supported with such traits to achieve genetic gain in PRG drought tolerance through selection. Interestingly, correlated response to selection estimate of OP and RDWD was higher than the ΔGc estimates of both traits, suggesting that one trait would be adequate  as a joint selection criterion alongside WUEA to improve PRG drought tolerance.In practice, laborious trait measurements including WUEA‐OP mechanism even at lower LWP levels . Supporting this, A showed genetic potential with high hn2 and significant rg estimates with SC, ET, OP, WUEA, and WUEi traits at both MS2 and MS3. Nevertheless, hn2 estimates of WUEAET and WUEASC were statistically nonsignificant for the data measured at both MS2 and MS3, indicating their nonheritable nature under imposed drought. This may be partially because the hn2 of SC was significant but additive genetic variance was statistically nonsignificant especially at MS3  compared with those of WUEASC and WUEAET under the conditions tested. Our results further showed that selecting PRG plants for certain component traits of the WUEA‐OP trait mechanism would be advantageous in sustaining SMC retention for drought tolerance. Notably, OP or RDWD can be excluded from the selected trait combination as their rg and correlated response to selection estimates were exceptionally high and thus selecting PRG genotypes for one trait will naturally bring in the other. In previous research, hn2 and ∆Gc estimates of OP of PRG were found to be significantly high and consistent , investigation of the potential role of mesophyll conductance in the WUEA‐OP trait association, characterization of the QTLs of WUEA, and evaluation of the relevance of these results to forage species other than PRG are highly desirable.In summary, our results favored selecting PRG for WUE Thomas,  while weNo potential conflict of interest was reported by the authors.Appendix S1.Click here for additional data file.Appendix S2.Click here for additional data file.Appendix S3.Click here for additional data file.


What are the major sources of worldwide variability in subjective well-being (SWB)? Twin and family studies of SWB have found substantial heritability and strong effects from unique environments but virtually no effects from shared environments. However, extant findings are not necessarily valid at the global level. Prior studies have examined within-countries variability but did not take into account mean differences across nations. In this article, we aim to estimate the effects of genetic factors, individual environmental exposures, and shared environments for the global population. We combine a set of knowns from national well-being studies (means and standard deviations) and behavioral-genetic studies (heritability) to model a scenario of twin studies across 157 countries. For each country, we simulate data for a set of twin pairs and pool the data into a global sample. We find a worldwide heritability of 31% to 32% for SWB. Individual environmental factors explain 46% to 52% of the variance (including measurement error), and shared environments account for 16% to 23% of the global variance in SWB. Worldwide, well-being is somewhat less heritable than within nations. In contrast to previous within-countries studies, we find a notable effect of shared environments. This effect is not limited to within families but operates at a national level. What makes the worldwide population of 7.8 billion humans differ in subjective well-being (SWB)? What roles do genes and unique and shared environments play? Well-being is a fundamental human value  and a UNHumans have migrated around the globe for thousands of years, and the current global population lives in a wide variety of contexts and life conditions. People may live as a nurse in Kyiv, a refugee in Raipur, a hunter in Xingu, or a barber in Seville. Each and every one has a particular genetic makeup and some unique life experiences and exposures and shares certain environmental factors with other people . Two ranThere is solid evidence for genetic influences on well-being. Twin and family studies have typically reported heritability estimates in the .20 to .50 range , and metGenerally, behavioral-genetic studies have found little evidence of shared environmental factors in psychological traits. The notion that most environmental effects are unique rather than shared in families is listed among the top 10 replicated results in behavioral genetics: “The basic finding that most environmental effects are not shared by family members remains one of the most far-reaching findings from behavioral genetics” , p. 13. World Happiness Report  as our starting point. Because this is not a fixed statistic but, rather, a mean heritability with variability, we set out to model scenarios in which countries vary around this mean. Moreover, in addition to these estimates for subjective well-being in general, World Happiness Report (worst possible life (0) to best possible life (10). We used the reported means and standard deviations for each country as a best estimate of current levels and variability for the 5-year period of 2015 to 2019. A total of 157 countries had valid data for this period , representing 81% of all 195 countries. Note that the World Happiness Report publishes 3-year averages but also provides information about yearly scores. Finally, population sizes were downloaded from worldofmeters.info, with data from the United Nations Population Division estimates.Second, the s Report  providess Report , in whicWorld Happiness Report uses the Cantril Ladder , the World Values Survey, and other national studies have provided evidence for the validity of cross-cultural comparisons  is given by twice the difference in the MZ versus DZ correlation. The shared environmental variance is given by twice the DZ correlation minus the MZ correlation. Thus, a C factor is indicated only when the DZ correlation exceeds half the MZ correlation. When the DZ correlation is less than half the MZ correlation, nonadditive genetic effects are indicated. Such effects include within-alleles effects (dominance) or between-alleles effects  are typically used to obtain fit measures, confidence intervals (CIs), and comparison of alternative models and to test more complex multivariate models. For our purposes, the Falconer’s formulas (above) are sufficient and provide for transparent communication of results.r = .38 and with means and standard deviations as reported for the specific country. Correspondingly, similar variables were created for DZ twin pairs, but with a twin–cotwin correlation of .19. The correlations of .38 and .19 are based on the equations above; that is, these would be the observed correlations that result in the extant heritability estimates . Most analyses were conducted for averages (of means and standard deviations) over 5 years (2015–2019), but we also provide estimates for specific years.We simulated twin data for each of the countries with known means and standard deviations in the Gallup World Poll . For MZ The simulated data for all countries were pooled to provide a data set mimicking the scenario of having worldwide twin data. Next, we calculated twin–cotwin correlations for MZ and DZ pairs in this worldwide data set. Finally, this simulation was repeated 100 times (as a Monte Carlo simulation) to reduce random effect and obtain precise parameter estimates and standard errors.Supplemental Material available online, along with an elaboration of technical aspects of setting up the twin data.To examine the robustness of findings, we simulated five scenarios involving different assumptions and input parameters. First, in our basic scenario, we simulated samples of 1,000 MZ and 1,000 DZ twin pairs in each country  and used A = .38 and E = .62 as input parameters. Second, to account for the differences in country sizes, we adjusted sample sizes in each country to reflect their proportion of the global population. Third, we included a C effect to reflect the presence of regional differences within countries in addition to the between-countries differences. We also provide results for each year. Fourth, we tested a scenario with within-countries heritability = .32 (rather than .38), based on the meta-analytic findings of the specific well-being component of life satisfaction. Finally, we adjusted for random measurement error, which typically is contained in the E-parameter, and provide corresponding results. All R code for the simulations can be found in the Supplemental Material). Simulations were set up so that the means and standard deviations were identical to the empirical values for each country. As is shown, there are cross-country variability in both means and variances.r ≈ .38 (MZ) and r ≈ .19 (DZ). The left-side figures are from one simulation, and all repetitions yielded similar images. Because there is randomness inherent in the simulated data, the MZ rs varied between .28 and .48 across countries . Correspondingly, rs for DZ twins varied between .06 and .31. This variability mimics the variation in heritabilities across samples and countries and is manifested in the lines not being perfectly parallel. The blue lines (left) show associations for the pooled worldwide sample. As is shown, the slopes are steeper than the corresponding within-countries lines for both zygosity groups.rw was .50, and the corresponding correlation for DZ twins was .34. Thus, both the MZ and DZ correlations increased compared with the initial values of .38 and .19, respectively. Note also that the worldwide DZ correlation exceeds half the MZ correlation, implying the presence of a C factor, unlike in the within-countries data.For MZ twins, the estimated worldwide twin–co-twin correlation On the basis of the estimated correlations, we calculated the implied A, C, and E components. To examine the sensitivity and robustness of findings, we conducted analyses reflecting different scenarios.The initial analyses (above) are based on a situation in which we sampled 1,000 twin pairs (for each zygosity) in every country and then merged the data sets into a worldwide sample. However, countries vary in population size and represent different proportions of the total world population. Thus, we simulated a scenario in which sample sizes varied across countries to reflect each country’s proportion of the global population. To retain comparability with the first set of analyses, we used the same total sample size.The results were highly similar to those obtained in the initial unweighted analysis: A = .32 , C = .16 , and E = .52 . Thus, when taking into account the different population sizes, we observed a slightly higher genetic effect and slightly lower shared environmental effect but generally converging findings.The two previous scenarios are both based on the absence of a C factor in prior (within-countries) twin studies. However, given limited power to detect small C effects in typical sample sizes, such effects could theoretically still be present. In addition, there are other national studies that have reported within-countries regional differences, which would imply a certain C factor . TherefoThe worldwide simulation yielded the following results: A = .31 , C = .23 , and E = .46 . Again, we see the same level of genetic effects as in the previous scenarios, an added effect from shared environments, and slightly reduced effect from unique environments.Adding to the robustness, we also repeated the simulations of this scenario with separate analyses for each year in the observation period. In Scenarios 1 through 3, we used A = .38 and E = .62 as within-countries input for the simulations based on the meta-analyses of subjective well-being. Because one meta-analysis  also incFinally, we recalculated the A, C, and E components while adjusting for measurement error, again taking Scenario 2 as a starting point. Random measurement error is included in the E component and needs to be subtracted to provide adjusted estimates. Given a reliability of around .80 for the well-being measures , the worTo summarize, for all the alternative scenarios, we observed two key findings: Compared with extant within-countries findings, a worldwide perspective on well-being leads to (a) identification of a shared environmental factor explaining around 20% of the variance and (b) a corresponding reduction in heritability and effects from the nonshared environment.We set out to examine the major sources of worldwide variability in SWB. To what extent are differences in human well-being due to genetic differences, shared environmental factors, or unique environmental factors? Previous genetically informed studies on well-being have shown substantial genetic effects, and meta-analyses have reported weighted average heritabilities of .36 to .40 for subjective well-being in general . EnvironThe current analyses revealed a worldwide effect of the shared environment accounting for 16% to 23% of the variance in well-being, corresponding to betas of 0.40 to 0.48 as regression coefficients. This finding contrasts prior findings of virtually no shared environmental effects . We arguShared environments are defined as everything that contributes to similarities between twins or other family members . These fStatistically, our findings may serve as a proof of concept that between-countries mean differences will come out as a shared environment factor in twin analyses when data sets are pooled into a worldwide sample. We believe this may be obvious to some but unknown to many. Across the different models tested, the shared environmental effect was around 20%. The robustness of this effect is not a chance finding but, rather, due to the proportion of between-nations to within-nations variance, and similar figures may be obtained by direct calculations. A main advantage of performing a simulation study of twins is to put the findings in a context of previous genetically informative studies and to translate the between-countries differences into a component of shared environmental variance. Future research should seek to collect twin data from more countries and cultures to provide empirical evidence for the role of country-level factors.The need for joint consideration of group means and variances in behavior-genetic studies was recently pointed out . HoweverThe twin meta-analyses used as input  did not reveal any shared environment factor. However, other evidence  points tWe found that worldwide well-being is somewhat less influenced by genetic factors than what is found within nations. This is not surprising given that heritability is a relative variance statistic, referring to the genetically caused variance as a proportion of total variance . As the The notion of heritability as a relative statistic should be understood in contrast to the concept of absolute genetic variance. If the environmental variance increases and the absolute genetic variance remains the same, the heritability decreases. In a recent study, Across the models, the nonshared environmental factor was a large contributor to well-being worldwide. That is, despite substantial effects of national-level shared environments and individual genetic factors, a main source of well-being is still the unique experiences of each individual. These experiences are not shared with fellow citizens of one’s country or with one’s  MZ co-twin and contribute strongly to human well-being. Thus, twin studies represent a design that provides compelling evidence for the causal effects of environments and unique life stories.What are the implications of our approach beyond the field of well-being? The logic of country-level factors operating as a shared environment is valid across traits and disorders. Yet we would expect to find a similar presence of shared environmental factors only for conditions showing country-level differences in means or prevalences. For example, if suicide rates differ across countries but schizophrenia prevalences do not, only the former would come out with a shared environmental component in a worldwide perspective.The notion of geographic, country-specific clustering of shared environmental effects may also be translated to a notion of similar effects across time. For example, for adult intelligence, studies have reported substantial heritability and limited effects of shared environments . HoweverOur simulations and the twin meta-analyses they build on do not examine gene-environment interplay. Such interplay, including both correlations and interactions, is generally important across phenotypes and is increasingly being subject to study in well-powered samples. For example, a recent molecular-genetic study of socio-economy found evidence for gene-environment correlations involving polygenic scores and geographic regions . AnotherFor well-being, our findings of a worldwide C factor raise a set of new questions about how genetic dispositions interact with culture, national economy, governance, and other country-level factors (G × C interaction). For example, cultures may differ in how they value different human characteristics . Consequently, to the extent that these features are genetically influenced, the specific genetic factors contributing to well-being may vary across these cultures. Moreover, countries may differ in the extent to which they provide opportunities for genetic potentials and resources to be expressed and developed. For example, providing equal opportunities for education may allow genetic potentials for learning to flourish and hence potentially contribute to well-being for individuals and societies.There are some limitations inherent to the current study. First, our simulations are based on recent empirical evidence from two main sources: meta-analyses of twin studies  and natiIn summary, previous studies have shown substantial heritability and virtually no effect of shared environmental factors on human well-being . We arguIn a changing world, with pandemics, climate change, political polarizations, and wars, we believe findings on key sources of worldwide variability in well-being provide information relevant to health authorities, policymakers, and governments. As a UN Sustainable Development Goal , well-beClick here for additional data file.Supplemental material, sj-docx-1-pps-10.1177_17456916231178716 for Worldwide Well-Being: Simulated Twins Reveal Genetic and (Hidden) Environmental Influences by Espen Røysamb, Terrie E. Moffitt, Avshalom Caspi, Eivind Ystrøm and Ragnhild Bang Nes in Perspectives on Psychological Science


Although observational studies have established some socioeconomic traits to be independent risk factors for pelvic organ prolapse (POP), they can not infer causality since they are easily biased by confounding factors and reverse causality. Moreover, it remains ambiguous which one or several of socioeconomic traits play predominant roles in the associations with POP risk. Mendelian randomization (MR) overcomes these biases and can even determine one or several socioeconomic traits predominantly accounting for the associations.We conducted a multivariable Mendelian randomization (MVMR) analysis to disentangle whether one or more of five categories of socioeconomic traits, “age at which full-time education completed (abbreviated as “EA”)”, “job involving heavy manual or physical work (“heavy work”)”, “average total household income before tax (income)”, “Townsend deprivation index at recruitment (TDI)”, and “leisure/social activities” exerted independent and predominant effects on POP risk.We first screened single-nucleotide polymorphisms (SNPs) as proxies for five individual socioeconomic traits and female genital prolapse  to conduct Univariable Mendelian randomization (UVMR) analyses to estimate causal associations of five socioeconomic traits with FGP risk using IVW method as major analysis. Additionally, we conducted heterogeneity, pleiotropy, and sensitivity analysis to assess the robustness of our results. Then, we harvested a combination of SNPs as an integrated proxy for the five socioeconomic traits to perform a MVMR analysis based on IVW MVMR model.p = 0.004), but denied that of the other five traits on FGP risk . Heterogeneity analyses, pleiotropy analyses, “leave-one-out” sensitivity analyses and MR-PRESSO adjustments did not detect heterogeneity, pleiotropic effects, or result fluctuation by outlying SNPs in the effect estimates of six socioeconomic traits on FGP risk . Further, MVMR analyses determined a predominant role of EA playing in the associations of socioeconomic traits with FGP risk based on both MVMR Model 1  and Model 2 .UVMR analyses based on IVW method identified causal effect of EA (OR 0.759, 95%CI 0.629–0.916, Our UVMR and MVMR analyses provided genetic evidence that one socioeconomic trait, lower educational attainment, is associated with risk of female genital prolapse, and even independently and predominantly accounts for the associations of socioeconomic traits with risk of female genital prolapse.The online version contains supplementary material available at 10.1186/s12920-023-01560-5. However, these independent risk factors of POP have been challenged by some concurrent contradictive findings    where N strument .p threshold, by dividing 0.05 by the number of tests, which assumes each test is independent [p value less than Bonferroni-corrected p threshold as being statistically significant [p threshold but less than 0.05 was suggestive of statistical significance in the univariable MR analysis [Afterwards, we undertook a series of UVMR analyses to estimate the causal associations of genetically proxied socioeconomic traits with FGP risk using five MR methods, inverse variance weighted (IVW), MR-Egger, weighted median (WM), simple mode, and weighted mode . The IVWependent –54. We cnificant , 55, 56,analysis .2 (%)-values to quantitatively evaluate the heterogeneity level across individual SNPs [After forward and reverse directional UVMR analyses, we conducted heterogeneity, pleiotropy, and sensitivity analysis to verify whether heterogeneity and pleiotropy biased our UVMR results. First, we calculated Q-statistics and Iual SNPs . Thereafual SNPs . If one ual SNPs  to proviFurther, we used IVW MVMR method to disentangle which one or several of these socioeconomic traits predominantly accounted for the causal associations with FGP risk. Unlike UVMR analysis, MVMR analysis assumes that the IVs are strongly associated with at least one exposure, although not necessarily with each. To this end, we converged a combination of SNPs as an integrated proxy for the six categories of socioeconomic traits. Additionally, we performed feature selection for these six categories of socioeconomic traits using the “mv_lasso_feature_selection” function, and excluded one or more exposure traits with severe collinearity out of the subsequent MVMR analysis. Then, we performed MVMR analysis using “mv_multiple” function in R TwoSampleMR package. Similarly, ORs and corresponding 95% CIs were calculated for estimating causal effects. Bonferroni correction accounting for multiple testing was not tailed for MVMR analysis, since the latter has its inherent nature of mutual adjustment .https://shiny.cnsgenomics.com/mRnd/) [Moreover, we determined the statistical power in estimating causal effects of socioeconomic traits on FGP risk using a webpage-based application, the mRnd power calculator (m/mRnd/) .https://www.R-project.org) with the “TwoSampleMR”, “LDlinkR”, and “forestplot” Packages.All statistical analyses and visualization for results were performed using R statistical software  has a causal association with lower FGP risk based on IVW UVMR model. That is to say, the women possessing higher EA have approximately one-quarter less probability of incident FGP compared to their counterparts. However, the other five traits have no causal effects on odds of FGP . The results regarding causal associations of the eight socioeconomic traits with FGP risk by UVMR analyses based on three MR methods are demonstrated in Table p = 0.810; “job involves heavy manual or physical work”: OR 0.997, 0.981–1.012, p = 0.659; “average total household income before tax”: OR 1.004, 0.986–1.022, p = 0.669; “type of accommodation lived in: a flat, maisonette or apartment”: OR 0.998, 0.995–1.002, p = 0.377; “type of accommodation lived in: a house or bungalow”: OR 1.002, 0.998–1.006, p = 0.341; “Townsend deprivation index at recruitment”: NA; “leisure/social activities: pub or social club”: OR 1.002, 0.997–1.007, p = 0.473; “leisure/social activities: sports club or gym”: OR 0.998, 0.991–1.005, p = 0.596). That is to say, our analyses expel the possibility of reverse causality between genetically proxied FGP and any of eight socioeconomic traits.As shown in Table 2 = 35.171%, p = 0.027) and “leisure/social activities: pub or social club”  on FGP risk in UVMR analyses. Our “Leave-one-out” sensitivity analyses did not detect noticeable alterations in effect estimates when any one SNP was removed . Further, as shown in Table p = 0.137; “leisure/social activities: pub or social club”: PRESSO-adjusted OR 0.524, 0.023–11.765, p = 0.684). Remarkably, after PRESSO adjustments, we did not observe significant heterogeneity in the results for causal effects of income  and pub or club  on FGP risk. In addition, the results of effect estimates  for the six socioeconomic traits on FGP risk in UVMR analyses are visualized in six scatter plots  Model 1: “EA”, “heavy physical work”, “income “, “TDI”, and “pub or club”; (2) Model 2: “EA”, “heavy physical work”, “income “, “TDI”, and “sports”. We clustered 402 SNPs for Model 1 and 408 SNPs for Model 2 respectively, and used them as integrated proxy instruments to conduct MVMR analyses.Additionally, we performed LASSO feature selection for the two MVMR models using the “mv_lasso_feature_selection” function, and removed “income” due to its severe collinearity with “heavy physical work” in Model 1. Accordingly, the remaining four traits were retained. Dissimilarly, none of the five traits was detected to carry collinearity in Model 2.2-value) of phenotypic variations that were explained by our 408 integrated SNPs was 0.702% for EA, 0.718% for heavy physical work, 0.421% for income, 0.143% for TDI, and 0.108% for sports, respectively. Furthermore, we determined F-statistics symbolizing the total instrumental strength of our 402 SNPs (Model 1) and 408 SNPs (Model 2). Specifically, the former was respectively 2795.203, 650.161, 1147.773, and 1145.337 for the four traits, while the latter was 2753.746, 640.479, 969.778, 1130.869, and 1128.580 for the five traits. In summary, our findings supported strong, robust, and reliable genetic proxies for socioeconomic traits to investigate their causal associations with FGP risk.First, we calculated the proportion of phenotypic variances that were explained by our two clusters of SNPs and revealed that 402 SNPs as IVs for MVMR Model 1 explained 0.690% of phenotypic variances for EA, 0.702% for heavy physical work, 0.142% for TDI, and 0.117% for pub or club, respectively. As far as MVMR Model 2 was concerned, the proportion  has protective effect on FGP risk, and predominantly accounts for the associations between the four socioeconomic traits and FGP risk after adjusting for “heavy physical work” , and “pub or club” . In other words, the women who achieve higher EA have one-seventh lower predisposition to incident FGP than those own lower EA, regardless of the involvement of heavy physical work in their jobs, TDI, and leisure activities such as attendance to social pub or club.The results of our MVMR analyses based on Model 1 are displayed in Fig. p = 0.012) has protective effect on FGP risk, and plays a predominant role in the associations of the five socioeconomic traits with FGP risk, independent of heavy physical work , income , TDI , and sports . That is to say, the women with higher EA are one-quarter less likely to have incident FGP than those with lower EA, regardless of the involvement of heavy physical work in their jobs, income, TDI, and engagement in such leisure activities as sports.The results of our MVMR analyses based on Model 2 are also exhibited in Fig. The results of statistical power for the nine groups of integrated SNPs in estimating causal associations of socioeconomic traits with FGP risk are presented in Table There have been a variety of characteristics used as indicators measuring socioeconomic status in health research , 62, incOur UVMR analyses based on IVW method revealed a causal association of lower EA with higher FGP risk, suggesting that women with lower EA are more susceptible to have FGP than those with the opposite features. Moreover, our UVMR results are robust in consideration of no heterogeneity or pleiotropy in effect estimates. Besides, our results of reverse directional UVMR analyses expelled the possibility of reverse causality between FGP and any of the eight socioeconomic traits, which had been regarded as one of the major methodological limitations in previous observational studies.Our MVMR analyses further uncovered that among the four socioeconomic traits (in MVMR Model 1) and five socioeconomic traits (in MVMR Model 2), there was only one trait, namely, EA that independently and predominantly accounted for the causal associations of these socioeconomic traits with FGP risk.We speculated that higher educational attainment is associated with a lower probability of heavy physical work , higher Although it has been frequently found in previous observational studies that the decreased prevalence of POP is significantly associated with increased level of education by univariate analysis, there has been a controversy with regard to independent association of EA with the prevalence of POP. Some multivariate logistic regression analyses consistently evidenced that absent or insufficient formal education was an independent risk factor of having FGP or POP –9. Givenvia multivariate logistic regression analyses. Therefore, some researchers recommended avoidance of carrying heavy objects for attenuating POP risk. In contrast, Devkota’s [Jobs needing heavy physical labor, such as heavy load carrying, are ubiquitous activities for women living in low-income countries. In previous observational studies, there have been existing discrepant findings regarding whether strenuous work is independently associated with prevalence of POP. Some studies concluded that strenuous work –13 was aevkota’s  multivarp < 0.001). The mechanism underlying the associations between income level and FGP risk in observational studies remains unexplained, which can be interpreted partially by previous findings that the women with lower income are more predisposed toward a higher probability of early marriage [UVP, the main constituent of FGP, is a major cause of mortality and morbidity among women in low-income countries such as Nepal . Woodmanmarriage –86, vagimarriage –91, and marriage , 92, 93.The TDI is defined as an area-based measure of socioeconomic deprivation and regarded as a proxy of individuals’ socioeconomic deprivation, with a higher TDI referring to a higher level of socioeconomic deprivation . Also, tScientists have long known that the socioeconomic conditions in which children grow up impact their health behaviors in adulthood —particularly physical activity . ParticiBased on the complexity of the causal pathways resulting from social factors , such asOur UVMR and MVMR findings have important implications for public health by providing new insights into the pathogenesis underlying female genital prolapse (FGP) from a genetic perspective. Our study results add to the existing research on the association between FGP and socioeconomic status and suggest that modifying specific socioeconomic traits, such as promoting education among girls, may serve as a potential prophylactic measure against FGP. It is essential to extend educational attainment and initiate public health education programs, especially in countries where education is not widely available, as this can have a long-term impact on preventing FGP beyond simply raising people’s incomes. While it may be challenging to change educational attainment in adulthood, screening for FGP should be prioritized among women with lower educational attainment from a preventive perspective and early diagnosis standpoint.There are several strengths in the present study. To the best of our knowledge, our study is the first MR study focused on causal associations of multiple socioeconomic traits with FGP risk using large-scale GWAS data. Undoubtedly, our UVMR and MVMR analyses are superior over previous observational studies because we extract summary data from GWASs with a much larger sample size and a huge number of SNPs. Moreover, the results are robust and reliable by virtue of no heterogeneity or pleiotropic effect. Extremely importantly, our two MVMR models prove to be strong, robust, and reliable genetic proxies for socioeconomic traits.We confess several limitations in our study. Above all, although FGP predominates in female POP population, FGP is not exactly equal to POP. Owing to no GWAS available for POP, we had to resort to FGP as an outcome event instead of POP. It is not sound enough to implement comparisons of conclusions regarding associations of socioeconomic traits with two different, even if approximate, outcome events between observational studies and MR analyses. Secondly, a weak statistical power of our SNPs surrogating compromises the precision and reliability in estimating causal effect on FGP risk. Therefore, our conclusions should be used with caution. The unfavorable statistical power will not be improved unless the advent of updated GWAS datasets with a larger sample size and more eligible SNPs representing socioeconomic traits and FGP in the future. After all, we acknowledge that this study provides insights into the causal effect of EA on POP from genetic perspective. Still, the underlying mechanisms behind this causal relationship remain elusive and are worth further exploration in, perhaps, a mediation effect analysis of heavy physical work, income, TDI, and social activities, or in a pathway analysis involving molecular mechanisms and hormonic factors in the future.To sum up, our UVMR and MVMR analyses provided genetic evidence that as a representative of socioeconomic traits, educational attainment is causally associated with risk of female genital prolapse, and even independently and predominantly accounts for the association of socioeconomic status with risk of female genital prolapse.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8Supplementary Material 9Supplementary Material 10Supplementary Material 11Supplementary Material 12Supplementary Material 13Supplementary Material 14Supplementary Material 15Supplementary Material 16Supplementary Material 17Supplementary Material 18Supplementary Material 19


Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance.Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.The online version contains supplementary material available at 10.1186/s12863-023-01174-x. Phenotypes are the result of the individual genotypic value, environmental deviation, and interactions between genotype and the environment. The genotypic values can be divided in additive  and non-additive genetic effects due to interactions between alleles at the same locus (dominance) and two or more loci (epistasis) . Non-addAlthough breeders are mainly interested in the estimates of breeding values, fitting non-additive genetic effects in the genomic prediction models can increase the accuracy of the breeding values –9. FurthNon-additive genetic effects are expected to have a greater impact in crossbred performance as compared to purebred populations, mainly due to heterosis –22. As pNon-additive genetic effects can be of different magnitude in purebred and crossbred populations –22, as wThe results obtained are presented separately for each of the two datasets evaluated.Variance components and heritabilities were estimated for five traits representing phenotypes routinely collected from birth to breeding in a single nucleus pig line population . Table 1Low dominance variance components were estimated, ranging from 0 to 0.0576 for T1, T2, T3, and T4, and ranging from 138.0280 to 171.8660 for T5. Notably, the dominance variance ratios across all traits were found to be small, accompanied by relatively large standard errors. T5 had the highest dominance variance ratio, which ranged from 0.0397 to 0.0437, while for the other traits it ranged from 0 to 0.0206. However, the estimates of additive-by-additive epistatic variance were notably higher than the dominance variance estimates. These ratios were approximately 0.12 for T1, 0.24 for T2, 0.28 to 0.43 for T3, 0.09 for T4, and 0.13 to 0.15 for T5, indicating a more substantial contribution of additive-by-additive epistasis to the variance components for these traits.In general, there was a greater decrease in the additive genetic variance when additive-by-additive epistasis was included in the models . However, for T1 the additive genetic variance estimates also presented larger standard errors  based on Spearman’s rank correlation, as shown in Table The values of Akaike Information Criterion (AIC) Table S suggesteWe also estimated variance components and heritabilities for heat tolerance indicators in a crossbred (Large White x Landrace) population. Table As observed for the purebred population (Dataset 1), the inclusion of non-additive genetic effects in the model did not affect the residual variance estimates for most of the traits, except for HD Table . HoweverFor the crossbred population, the models also presented similar values of Akaike Information Criterion (AIC) Table S. LRT Ta for the We aimed to estimate variance components and evaluate the predictive ability of genomic models including non-additive genetic effects for different traits in purebred and crossbred populations. In general, we observed small estimates for dominance variance and the inclusion of additive-by-additive epistasis in the model reduced the estimates of additive genetic variance. The inclusion of non-additive genetic effects in models for genomic prediction resulted in changes in animals’ ranking and in the groups of animals to be selected. Since two different independent populations were used in this study, the One way to account for inbreeding depression in genomic prediction is by fitting the inbreeding coefficient as a covariate in the model. The benefit of doing so is that unbiased variance components may be obtained because the variance component estimates may be inflated when inbreeding or heterozygosity are not fitted within the models –19. AccoThe heritability estimates obtained from the models including only the additive and dominance genetic effects (MAD) corroborate with the estimates reported in previous studies using the same public dataset . The stuThe method to obtain the relationship matrices applied in this study, proposed by Vitezica et al. , is a flAs summarized by Wade et al. , for twoAll models presented dispersion close to one, indicating that there is no inflation or deflation for the genomic estimated breeding values (GEBVs) . The GEBAlthough models including epistatic effects were significantly better (based on LRT parameter) for some traits , including epistasis had lower GEBV accuracies for traits with lower heritability . It is noticeable that models including epistatic effects had greater standard errors for the additive genetic variance and heritability estimates for T1, which is the trait with the lowest heritability. This suggests that the available data might not be sufficient to effectively distinguish between additive genetic and epistasis effects. Therefore, larger datasets may be required to obtain more accurate estimates of non-additive genetic effects , particuAnother factor influencing this inferior accuracy for models including epistasis may be due to the non-orthogonality between the components, especially between additive genetic effects and additive-by-additive epistasis. At the same time, when only additive genetic effects were fitted in the models, part of the additive-by-additive epistasis was captured by the additive genetic component, as previously reported in other studies , 48. EveIn general, models including epistasis presented the largest changes in ranking of the animals in comparison to the additive model. According to Piccoli et al. , the degSince all heat stress-related traits had substantial additive genetic variability, including them in a selection program is feasible and can lead to genetic progress. Although, it should be noted that phenotypes measured in crossbred animals are expected to be more influenced by non-additive genetic effects than in purebred populations , 22. SmaBesides HD and PS having considerable epistasis estimates, models including epistatic effects were significant better (based on LRT parameter) only for PS and no significant dominance variance was observed for any trait related to heat stress in the crossbred population. However, HD and PS are categorical traits and, threshold models –56 shoulEstimating non-additive genetic variances is also difficult, since they are often, or at least partially, confounded with other effects, such as common environmental or maternal effects . In thisThe findings from this study suggest that the gene actions for heat stress-related traits in this population are mainly additive, or at least most of the non-additive genetic effects are captured by the additive genetic component. Although the dataset used in this study came only from crossbred animals, the pure breeds used for crossbreeding are both considered maternal breeds (Large White and Landrace) and genetically related –59. ThusQuantitative traits are controlled by many genes with additive and potentially non-additive gene actions, and their phenotypic expression are the result of many developmental and biochemical pathways comprised of loci networks that interact at the genetic and molecular levels, generating the epistasis effects . Thus, tMany studies have suggested that non-additive genetic effects are greater in traits related to fitness and adaptation than in morphological traits –65. ConsIncluding dominance effects in genomic prediction models did not improve the predictive ability of the models and most traits had none to low dominance variance. In the purebred population, low dominance variance ratios were observed, potentially due to reduced heterozygosity. Although models including additive-by-additive epistasis effects in genomic prediction exhibited significantly better fit for some traits, it led to lower accuracy when genomically predicting breeding values for traits with low heritability. For the evaluated populations and models, the inclusion of non-additive genetic effects did not improve genomic prediction accuracy. The non-orthogonality between variance components, especially between additive and additive-by-additive epistasis, suggests the presence of linkage disequilibrium challenges in the partition of additive and non-additive genetic effects. Therefore, there is still a need for developing methods able to accurately estimate non-additive variance components without assuming linkage equilibrium.Traits related to heat stress in the crossbred population had substantial additive genetic variability, indicating their suitability for inclusion in selection programs. The difficulty in estimating non-additive genetic effects and the confounding with other effects, such as permanent environmental effects, is a challenge to obtain accurate estimates. Models incorporating non-additive genetic effects in genomic prediction for heat stress-related traits did not perform better than the additive model for most of the traits evaluated. Future studies should use larger datasets when they become available, including both purebred and crossbred animals, to better understand the contribution of non-additive genetic effects, particularly for traits related to adaptation, welfare, and resilience.We used a public dataset  includin−5). After quality control, 40,828 SNPs and 3,534 individuals remained for further analyses. No animals were removed during the quality control due their genotypes were imputed [The quality control of genotype data consisted of removing SNPs with call rate lower than 0.90, minor allele frequency (MAF) less than 0.01, and extreme deviation from Hardy-Weinberg equilibrium  and w). The GEBV dispersion is the slope of the regression  method . LR methThe accuracy was obtained as:The AIC  LRT parareduced model − LogL complete model),LRT = − 2 and complete models , respectively. The level of significance used was 0.05.in which LogLWe also evaluated the impact of the model used on the ranking of the animals and the proportion of commonly selected individuals based on the GEBVs from different models. The Spearman’s rank correlation was calculated to evaluate the re-ranking between the GEBVs from the MA model, which is the most used approach for GEBV calculation, and the other evaluated models. Assuming different selection intensities, the top 1%, 5%, 10%, and 20% of the animals were ranked according to their GEBVs based on the MA model and compared with the animals selected based on the models including non-additive effects.ES), shoulder (TSS), rump (TRS), and tail (TTS); vaginal temperature considering the whole data measured every 10 min , and the average of the six records per hour corresponding to 08:00, 12:00, 16:00, and 20:00 h during four days (TV4days); respiration rate (RR), panting score , and hair density  . All anAs performed for Dataset 1, we also fitted different models including or not non-additive genetic effect of dominance and epistasis to estimate variance components. However, we did not include additive-by-dominance and dominance-by-dominance epistasis effects to avoid overparametrized models, since this second dataset is small.For the trait HD, the models MAI, MAID, MAIDE1, and MAIE previously mentioned were used. For skin temperatures, vaginal temperatures, PS, and RR repeatability models were fitted:MAIpe: MAIDpe: MAIDEpe: MAIEpe: Vall, the fixed effects were the concatenation of week and day of measurement; parity; concatenation of barn type and room; and in-barn environmental temperature as a linear covariate. For TV4days, the fixed effects were concatenation of week, day, and time of measurement; parity; days in lactation; concatenation of barn type and room; and in-barn environmental temperature as a linear covariate. The fixed effects fitted for skin surface temperature  and RR were trait recorder; concatenation of week, day, and time of measurement; parity; days in lactation; concatenation of barn type and room; and in-barn environmental temperature as a linear covariate. For PS, the fixed effects were trait recorder; concatenation of week and day of measurement; parity; days in lactation; concatenation of barn type and room; and in-barn environmental temperature as a linear covariate. The fixed effects fitted for HD were trait recorder and parity.where s et al. . For TVaThe variance components for dataset 2 were estimated following the same methods described for dataset 1. The analyses were also performed using ASREML software . The estThe AIC  and LRT Additional file 1: Table S1. Akaike information criterion (AIC) of model comparison for the purebred pig population.Additional file 2: Table S2. Likelihood ratio test (LRT) of model comparison for the purebred pigs dataset.Additional file 3: Table S3. Akaike information criterion (AIC) of model comparison for the crossbred pig population.Additional file 4: Table S4. Likelihood ratio test (LRT) of model comparison for the crossbred pig dataset.


The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models.An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods.The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness.The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits. A proloThe water dissipation of maize ear is closely relevant to its husk, which consists of multiple layers of leaves tightly wrapping the ear. Maize husk protects the ear from pests, diseases and dehydration during the ear formation process, on the one hand; and it impedes water dissipation from the kernels during the maturity stage decreasing the quality of mechanical harvesting, on the other hand. The water diffusivity of the husk is affected by its architecture, including husk length (HL), husk width (HW), husk thickness (HT), husk layer number (HN), and husk tightness (HTI) . RelativMarker-assisted selection (MAS) and Genomic selection (GS) are both used in plant and animal breeding programs to speed up and improve the accuracy of breeding to develop new and improved cultivars or breeds. The MAS is underlain by the establishments of significant linkages between special DNA sequences  and the chromosomal loci of the genes controlling the traits to be selected. The MAS approach cannot be used to select the quantitative traits affected by many small-effect genes. Consequently, the application of MAS on the selection of complex quantitative traits in the breeding is severely constrained . The GS There are many factors that affect the accuracy of GS prediction, including genome sequencing platform, statistical model, ratio of the training population to testing (breeding) population, population genetic structure and molecular marker density throughout genome. With the constantly striving, more varieties of sequencing approaches have been developed and the acquisition of genotyping data is more convenient. Genotyping by sequencing (GBS) , RNA seqThe ability of GS prediction is also affected by mathematical method of modeling. The fixed regression-least squares (FR-LS), random regression-BLUP (rrBLUP), genomic best linear-unbiased prediction (gBLUP) and Bayesian methods are commonly used statistical methods for GS modeling . gBLUP a2 is commonly used to evaluate whether the marker coverage is suitable for GS. For traits with higher heritability, a suggested average adjacent marker r2 = 0.15 is sufficient to achieve high prediction ability; but for low heritability traits, r2 is required to increase to 0.2  of Hainan Province in 2015 (15SY) and 2016 (16SY), in Fushun  of Liaoning Province in 2016 (16FS) in China. The experiments were carried out in a randomized complete block design with two replications in each environment . All plaThe kernel moisture content is expressed using AUDDC (area under the dry-down curve) . The insWhere n is the number of measurements; i is the ith measurement time; γ is the converted meter reading and t is the number of days after pollination.The husk tightness (HTI) can be calculated by the formula:Therefore, the larger the HTI value is, the looser the husk is. For obtaining the loose husk perimeter, soft meter rulers were used to measure the perimeter of the middle of the maize ear husks under the natural growth state; the tight husk perimeter was measured by tightening the husk so that it tightly surrounds the ear. The detail of investigation for HTI was described in th day after pollination, and the measurements were repeated 6 times for each line. The final calculation was carried out according to the protocol reported in our previous work (http://maizego.org/Resources).HTI index was measured at 50ous work . In addi2.2Pearson correlation coefficients between the HTI and the AUDDC were calculated by cor function in stats package in R program to define the effect of HTI on maize grain water content. The phenotypic variation of HTI was analyzed using R software 3.5.3.The best unbiased linear predictor (BLUP) was calculated for the phenotypic data of the three environments of HTI, and the final BLUP value was obtained by adding the mean value to the estimated value through a mixed linear model:i is the environmental effect of the i-th environment, and r(e)ij is the effect of the j-th replication in the i-th environment, fk is the genotypic effect of the k-th individual, where i, j and k represent the number of environments, replications, and individuals, respectively; μ is the overall average of HTI, eThe proportion of the total variance explained by the genetic variance is defined as heritability. Heritability was estimated by the PROC MIXED program in SAS software . The broad sense heritability was calculated as:In this formula, e represents the total number of environments, and r represents the number of replicates of the lines contained in each environment. Where ironment . Among tironment .2.3http://maizego.org/Resources).The association panel was genotyped using five methods, including four sequencing platforms, the Illumina maize SNP50 array (50K), the Affymetrix Axiom Maize 600K array (600K), genotyping by sequencing (GBS), RNA sequencing (RNA-seq), and an integrated SNP maker data set (Integration). The integration set was synthesized by three steps: (1) merging the data from the MaizeSNP50 BeadChip  and the All genotype data were preliminarily filtered in TASSEL v5.2 according to the adoption criteria of MAF>=0.05 and missing rate<=0.2. Then, the missing values in the preliminary filtered data by TASSEL v5.2 in the beagle v5.2 software were filtered again in TASSEL according to the same adoption standard. After the intersection comparison between the filtered genotype data, the remained SNP markers from the GBS, 50K, 600K, RNA-seq and Integration were 887, 47368, 436972, 524320 and 1243316 , respectively. At last, the inbred lines with both the genotype data and phenotype data were 380, 438,133, 315 and 438 lines, respectively, for the GBS, 50K, 600K, RNA-seq and Integration. The marker data from 600K were not used in the subsequent analysis, because of less available inbred lines genotyped.2 = 0.01, 0.1, 0.2, 0.5, 0.8, 1.0 and the left number of markers were: (1) 940, 52098, 131428, 333074, 569514 and 1243316, respectively, for the Integration; (2) 113, 8485, 18292, 29537, 35964 and 47368, respectively, for 50K; (3) 428, 4524, 33568, 111713, 172319 and 436972, respectively, for 600K; (4) 64, 676, 743, 801, 817 and 887, respectively, for GBS; (5)388, 16182, 56582, 157164, 256702 and 524320, respectively, for RNA-seq. The larger the value of r2 is, the more SNP markers is remained.In addition, the plink software was used to filter the SNP loci of each genotype data from the different platforms with different linkage disequilibrium (LD) coefficient, that is, r2.4In this study, we used six models, rrBLUP (Ridge Regression Best Linear Unbiased Prediction), BayesA, BayesB, BayesC, BL and BRR in the R package BGLR  to prediWhere y is the observation vector; W is the design matrix relating lines to y, G represents the genotype matrix, μ is marker effects vector; The Bayesian model is as follows:k is the genotype vector of the kth SNP, and gk is the effect value of the kth SNP; e is the residual random residual vector.where y is the phenotype observation vector; b is the fixed effect vector, X is the fixed effect correlation matrix; q is the number of SNPs, zThe BGLR package is mainly used for the research of parametric and semiparametric Bayesian methods, which has built-in multiple Bayesian regression models and integrates multiple algorithms. Bayesian methods assume some prior distribution for the variance of marker effects and allow each marker to have a different effect variance . The dif2.5To explore the impact of the proportion of the training population in the association panel on the GS prediction accuracy, we took out 10% to 90%, at an interval of 10%, of inbred lines in the association panel to form the training population, that is, the size of the testing or predicted population is 90% to 10% of all inbred lines in the association panel.2.6We randomly took out 10% to 90%, at an internal of 10%, of the inbred lines from each subgroup  in the association panel as a training population for establishing the prediction model to evaluate the prediction abilities on: (1) the remaining inbred lines (as testing population) in the same subgroups; (2) the inbred lines (as testing population) from the remaining individuals of the same subgroups plus individuals from other subgroups. The prediction ability was calculated from the Pearson correlation analysis between true breeding value (TBV) and genomic estimated breeding value (GEBV) based on BLUPs from 100 replicates.2.7The prediction abilities of the marker data from different sequencing platforms were judged by the 80% of inbred lines in the association panel as the training population and the remaining 20% of inbred lines as the testing population . For more practical application, we used the 70% of inbred lines in the association panel as the training population and the remaining 30% of inbred lines as the testing population to explore the effects of the marker density. However, when studying the effect of population genetic structure on the prediction ability of GS, we still chose 10%-90% of the panel as modeling population. Both cross-validations were repeated for 100 times.33.1According to the HTI formula, a larger value of HTI represents a husk that is more loosely attached to the ear. The HTI values and corresponding AUDDCs demonstrated a significant negative correlation across the whole association panel, as well as in the NSS and MIXED subgroups for BLUP Figure 13.2To identify the most suitable statistical model and genotyping platform for GS of HTI, we conducted a comparison of the prediction accuracies of BayesA, BayesB, BayesC, BL, BRR, and rrBLUP models across the different genotyping platforms  Figure 2Using the most suitable rrBLUP model, we evaluated the prediction abilities of markers from different genotyping platforms for HTI Figure 23.3In general, the prediction ability of GS tend to increase as the proportion of individuals in the training population rises in an association panel. To investigate this trend, we randomly selected 10-90% of individuals as the training population and the remainder as the testing populations, to predict HTI using marker data from 50K, RNA-seq and Integration by the rrBLUP model. As shown in 3.42 value of 0.01, 0.1, 0.2, 0.5, 0.8, and 1.0. 70% of individuals in the association panel were selected as the training population, with the remaining 30% serving as the testing population. The results showed no significant differences in prediction abilities under most marker densities among the three genotyping methods, except for the density with r2 = 0.01  coefficient r2 = 0.01 Figure 43.52 of 0.01, 0.1, 0.2, 0.5, 0.8 and 1.0 from the three genotyping methods: 50K, RNA-seq, and Integration. The analysis involved the following procedures: (1) randomly selecting 10-90% at internals of 10% of individuals from different genetic structure populations with four subgroups as the training populations to predict the remaining individuals within the same populations; (2) randomly selecting 10-90% at internals of 10% of individuals from the four subgroups  as the training populations to predict the remaining of individuals within the same subgroup (within subgroup)  did not have a significant impact on the GS ability of HTI. These findings are consistent with a previous report in which the use of 5 to 27,000 markers for GS of traits showed that the prediction ability ceased to increase significantly once the number of markers exceeded 2,000 (2 = 0.01. Within the range of 0.01-0.1 for LD (On the one hand, marker density affects sequencing costs, and on the other hand, it impacts the prediction accuracy of GS . Therefoed 2,000 . Here, t4.5The genetic systems governing the agronomic traits vary among different maize germplasms . TherefoIn addition, this study has revealed that different subgroups possess unique characteristics in GS, as some subgroups perform better prediction performance within the same subgroup, while others excel in predicting across subgroups. Notably, GS on the SS subgroup showed a marked improvement within the subgroup as compared to the whole population. Our previous research has demonstrated that GS showed a higher efficacy for the traits of HN, HT and HW in both the whole association panel and SS subgroup . However52 = 0.1~1.0 had no significant effect on the GS ability, hence, genotyping methods with low marker density or low cost can be adopted for GS of HTI in maize. The genetic structure of the association panel had a significant impact on the GS ability of HTI. Therefore, GS based on the constituent subgroups of the breeding population is an important method to enhance the prediction ability. Understanding the genetic structure and inherent molecular mechanisms of HTI in various subgroups can contribute to molecular breeding in maize.This study demonstrated that the HTI of temperate maize germplasms in the association penal was significantly and negatively correlated with the moisture content of grains in the majority of tested environments. Additionally, the accuracy of HTI in GS was affected by the genotyping method, statistical model, proportion of training population in the association panel, marker density, and population genetic structure. The accuracy of HTI varied according to the genotyping method used among the four methods tested, the 50K and GBS were more cost efficient and adequate for average GS performance of HTI. The rrBLUP model was more suitable for predicting HTI comparing to other methods. The prediction accuracy of HTI increased with the proportion of training population in the association panel, but the amplification of prediction accuracy was negligible in the case of the SS subgroup at the proportion of training population ranging from 60% to 90% due to cost inefficiency. The marker densities with rhttps://doi.org/10.6084/m9.figshare.23694426.v1.The data presented in the study are deposited in the figshare repository, accession number 10.6084/m9.figshare.23694426. The web link is AZ, ML and ZC conceived and supervised the project. YL, SZ and MA conducted the experiments. YG, FZ and YR helped the bioinformatics and statistical analyses. YL, ZA and ZC wrote and revised the manuscript. FZ helped in data analysis and graph plotting. All authors contributed to the article and approved the submitted version.


TRIM2, MND1, TLR2, RNF175, CEP290, TMTC3, RERE, SLC45A1, SOX2, MORN1, SKI, FAAP20, PRKCZ, GABRD, CFAP74, CALML6 and TMEM52 genes as well as nine uncharacterized regions  were defined as plausible candidates. The findings of this study shed light on the genetics of wool quality and yield for the Akkaraman breed and suggests targets for breeders during systematic breeding programmes.The objective of this study was to uncover the genetic background of wool quality, a production trait, by estimating genomic heritability and implementing GWAS in Akkaraman sheep. The wool characteristics measured included fibre diameter (FD) and staple length (SL) at the age of 8 months and yearling fibre diameter (YFD), yearling staple length (YSL) and yearling greasy fleece weight (YGFW) at 18 months of age. Animals were genotyped using the Axiom 50 K Ovine Genotyping Array. Maximum likelihood estimations of a linear mixed model (LMM) were used to estimate genomic heritability, where GWAS was conducted following a score test of each trait. Genomic heritability estimates for the traits ranged between 0.22 and 0.63, indicating that phenotypes have a moderate range of heritability. One genome- and six chromosome-wide significant SNPs were associated with the wool traits in Akkaraman lambs. Accordingly,  Small ruminant breeding has played an important role in meeting human needs  for centuries . Sheep bAkkaraman is a fat-tailed dual purpose (meat and milk) breed, and it is the most commonly raised indigenous sheep in Türkiye ,6. PopulThe economic value of fleece is directly related to its fineness, length, strength, fibre elasticity and final weight. Within the clothing textile industry, lower diameter and strong resistance are preferred, as they prevent breaks which may occur throughout the production process. Wool quality parameters are mainly affected by the genetic background of the animals and environmental factors such as nutrition, climate and age . StudiesKRTCAP3, KRTAP9, KAP family, UBE2E3 and RHPN2) predominantly involved in keratin synthesis were suggested to play a role in fibre diameter, staple length and greasy fleece weight, representing the most economically important wool parameters [In the last two decades, genome research in farm animals has been accelerated by the advances in molecular genetics. Reference genomes have been published for domestic sheep and high density commercial arrays have been developed for genome-wide distributed single nucleotide polymorphisms (SNPs), which can be employed to map the relationship between economically important traits and genomic loci . Genomicrameters ,30. WhilIn total, 426 Akkaraman sheep  raised iAnalysis of fibre diameter and staple length required approximately 50 g of fleece sampled from the caudal aspect of the scapula. These were taken at 8 and 18 months of age and delivered to the laboratory in numbered sample bags. Samples sent to the laboratory were analysed in terms of fibre diameter and length using OFDA 2000 , which is an optical fibre diameter analyser instrument located in the Ankara Sheep and Goat Breeders Association Laboratory. During the analysis, a bunch of fibres were aligned and straightened from end to end and placed into the device. Following loading, fibres were automatically measured optically in micrometres (µm) for diameter and millimetres (mm) for length. At 18 months of age, the animals were sheared in a clean area and the resultant wool weighed using a scale with a precision of 10 g. In total, five wool traits were measured and recorded as following: fibre diameter at 8 months of age (FD), staple length at 8 months of age (SL), yearling fibre diameter (YFD), yearling staple length (YSL) and yearling greasy fleece weight (YGFW). The descriptive statistics of the traits are presented in p = 0.05/49,931) were removed from the analysis. Additionally, those SNPs that are mapped on the sex-linked chromosomes, mitochondrial chromosomes as well as the SNPs with unknown positions were also removed from further analysis. On the other hand, animals were removed from the analysis if samples had a call rate below 90% and a pairwise IBS rate higher than 95%.Genotyping was performed using the Axiom 50 K Ovine Genotyping Array  at the Genetics Laboratory of the International Centre for Livestock Research and Training (ICLRT) in Ankara. During genotyping, the Axiom 2.0 Assay 96 Array Format Manual Workflow protocols provided by the manufacturer were followed. Quality control (QC) of the raw genomic data was performed using the GenABEL package in the R programme . DetaileMissing genotypes were imputed before proceeding to genomic heritability estimation and genome-wide association analyses. SNPs that passed the QC criteria were imposed and imputed according to the current status of the SNP within the given population. The expectation-maximisation (EM) algorithm was used with the process carried out by the R GenABEL package .y is the vector of individual observations of each trait measured, β is the vector of fixed environmental effects , u~0, e~0, X is the design matrices mapping fixed effects and Z is the polygenic background effects of each observation. Within MVN calculation, I is the identity matrix and G represents the genomic relationship matrix derived by VanRaden [The animal linear mixed model and average information restricted maximum likelihood (AI-REML) approach provided by the ‘sommer’ package of R  was usedVanRaden . The folAfter estimating variance components for each trait, the narrow sense heritability estimates of each trait were obtained by using the formula given below:p-value) of the SNP effects for a sense of global genomic distribution. Q-Q plots of the SNPs and genomic inflation factor lambda , with the chromosome-wide threshold as 3.07 × 10−5  × 26).The same generalised model and genomic relationship matrix were used to estimate SNP effects in genome-wide association studies with a score test developed by Chen and Abecasis . In the approach . A consePositional information for SNPs associated with measured traits was ascertained using the Oar_v4.0 genome assembly and the NCBI Genome Data Viewer . Candida10 base, which removed the skewedness of the data and established a near normal distribution. The resultant statistical analysis of the phenotypes are presented in Preceding further analysis, phenotypes were checked for outliers. Measurements that were below or above three standard deviations were considered outliers and excluded from further analysis. The phenotypic data were also checked for normality, where only yearling fibre diameter (YFD) was found to slightly deviate from a normal distribution, which was transformed to provide a normal distribution. The log transformation applied to YFD prior to any further analysis basically changed the scale of the data into logGenotyping resulted in 49,931 SNPs called for the sample set. These raw data were filtered against various QC standards and 42,320 SNPs for each sample passed the imposed criteria. Therefore, further analyses were based on these 42,320 SNPs from varying animal numbers see , which dStatistical power was increased following quality control by imputation of genomic data for missing SNPs before genomic relationship matrix construction and association analyses. Expected genotype scores for imputation of missing SNPs were estimated based on the population mean as described by Chen and Abecasis . An animThe simple linear model was used to determine the effects of fixed factors before preceding to genetic parameter estimations and GWAS analysis. Accordingly, herd and age (in days) were found to be significant on fibre diameter and staple length. Meanwhile, greasy fleece weight was not significantly affected by fixed factors. Genetic parameter estimates  for traits and their standard errors are illustrated in p-values, associated genes and genome- or chromosome-wide significance. Type one errors may be imposed through multiple SNP testing; to avoid these errors, both genome- and chromosome-wide thresholds were calculated. Thresholds were determined using the Bonferroni correction test [−6  and the chromosome-wide significance threshold was set to 3.07 × 10−5  × 26). Lambda (λ) was calculated to check for the presence of inflation or deflation in the corrected p-values for SNPs. A visualisation of expected versus observed p-values are presented in GWAS results correlating measured wool production traits with significant SNPs are presented in ion test . AccordiTRIM2, MND1, TLR2, RNF175, CEP290, TMTC3, RERE, SLC45A1, SOX2, MORN1, SKI, FAAP20, PRKCZ, GABRD, CFAP74, CALML6 and TMEM52. Besides these defined genes, there were nine undefined regions isolated, namely LOC101118971, LOC105609137, LOC105603067, LOC101122892, LOC106991694, LOC106991467, LOC106991455, LOC105616534 and LOC105609719, as candidates. Importantly, two of the chromosome-wide SNPs did not associate with any defined or undefined genes.A total of seven SNPs, one genome- and six chromosome-wide, on sheep chromosomes (OAR) 1, 2, 3, 8, 12 and 17 were found to be significantly associated with the analysed traits. Notably, a significant SNP was not identified for the yearling greasy fibre weight (YGFW) phenotypic trait. Genes associated with significant SNPs were located by searching the Oar_v4.0 sheep genome assembly either 200 kb upstream or downstream of the significant loci through the use of the National Centre for Biotechnology Information (NCBI) Genome Data Viewer . AccordiAkkaraman wool has long been known to have coarse- and carpet-type wool. Most of the wool produced by Akkaraman sheep has been used for blanket, rug and carpet production since a couple of historical studies suggested that Akkaraman sheep have low quality wool ,8. HowevIn farm animals, most of the economically important traits are measured as a quantitative character. Importantly, quantitative characters tend to have complex structures because they are controlled by a large number of genes dispersed within the genome, the heritable component of variation. Therefore, it is essential to identify and understand the genetic parameters  responsible for altered animal characteristics to employ sustainable genetic improvement breeding programs. Notably, pedigree records are needed to calculate genetic parameter estimates of economically important traits and are significantly lacking for indigenous sheep breeds. Fortunately, deep pedigree records are not required to calculate present genomic relationships, which allowed the present study to estimate the genomic heritability of wool production traits. The five chosen traits within this study were fibre diameter (FD), staple length (SL), yearling fibre diameter (YFD), yearling staple length (YSL) and yearling greasy fleece weight (YGFW), which implemented animal mixed models using GRM to calculate the genetic parameters of interest.Fibre diameter heritability estimates were found to be 0.41 and 0.22 at 8 and 18 months, respectively. These values are quite low compared with the renowned fine-wool sheep breeds. In particular, prior studies on Chinese fine-wool sheep suggested a heritability of yearling fibre diameter of 0.64 ,41,42. HA second desired trait is a long staple length, where the increased length results in a higher yield during the industrial processing of the wool. Studies considering fine-wool sheep indicate that the heritability for staple length (SL) at first shearing varies between 0.23 and 0.66 in Merino and between 0.44 and 0.62 in Chinese fine-wool sheep ,41,42. FThe heritability estimate of yearling greasy fleece weight in Merino and its crosses has been found between 0.32 and 0.53 ,41,42. TThere may be two reasons for the high variation of heritability for fibre diameter and length at the different ages represented in our study population. Firstly, the data consist of the different sample sizes, 410 animals at 8 months of age and 78 animals at 18 months of age, and alternate sampling time frames. Another reason for high heritability estimate variance could simply be the physiological differences between the two stages of life, where the animal genetics or environment might be more effective in the determination of the trait at young versus adult periods. Nevertheless, it is clear that many factors  play a role in the differences of heritability estimates in studies conducted in different sheep breeds.Within the last two decades, GWAS has become a widely used method to uncover genetic mechanisms behind phenotypic expression and to identify genomic factors  that act on economically important traits in farm animals. Species-specific commercial SNP panels containing functional variants that span the whole genome are a relatively inexpensive and quick methodology used to obtain genomic information from animals. In our study, GWAS were carried out to reveal the genomic regions that affect the production and quality parameters of wool. An animal-mixed-model-based association test and genomic control were implemented to avoid any possible false positives. Overall, the used methods appear to be successful considering the Q–Q plots of all the analysed traits, as there is no major systematic inflation of the test statistics. The special case of the YFD and YSL  might be sourced by the low sample size, as well as the very highly polygenic nature of the traits, which is also supported by the non-association of any major SNPs regarding the traits of interest .rs399017576 was the SNP variant with the highest p-value (1.03 × 10−7) detected on OAR17 and was the only genome-wide significant SNP. This SNP is localised on an uncharacterised genomic region (LOC101122892). The most significant chromosome-wide SNP (rs416693666) was found on OAR3, near the centrosome protein 290 (CEP290) gene. This gene encodes a protein which localises to centrosomes and cilia. Annotated biological functions for CEP290 included cilium assembly (GO: 0060271), ciliary basal body-plasma membrane docking (GO: 0097711) and non-motile cilium assembly (GO: 1905515). Cilia are critical for hair follicle development. A study conducted on mice found that cilia are a key signalling component for normal hair morphogenesis and is needed for cells in the dermis to receive signals from hedgehog signalling pathways [CEP290 gene is a strong candidate for further analyses. During the association analysis for the yearling fibre diameter trait (YFD), a separate SNP was found to have chromosome-wide significance on OAR2; however, this SNP was not associated with any gene.As seen in pathways . The prers399379059 and rs420575132 on OAR1 and 12, respectively. rs399379059 is present within the arginine-glutamic acid dipeptide repeats (RERE) gene. This gene encodes an atropine family of arginine-glutamic acid dipeptide repeat-containing proteins. Meanwhile, the significant SNP associated with the staple length measured at 18 months (YSL), rs419440256, was located on OAR12. This SNP had a number of candidate genes identified, but protein kinase C zeta (PRKCZ) is of specific interest. The PRKCZ gene synthesises an enzyme which belongs to the PKC family of serine/threonine kinases. These kinases are involved in many different cellular process such as positive regulation of cell proliferation (GO: 0008284), cell surface receptor signalling pathways (GO: 0007166) and negative regulation of protein-containing complex assemblies (GO: 0031333). Various studies within humans have found prior associations between protein kinase C’s and hair follicular growth and structure  [The study of staple length at 8 months of age returned significance at SNPs ngation) .TMTC3 (transmembrane O-mannosyltransferase targeting cadherins 3) is associated with the 8-month fibre diameter phenotype and is a gene which encodes a protein belonging to the transmembrane and tetratricopeptide repeat-containing protein family. Identified biological processes of this protein include protein glycosylation (GO: 0006486), protein O-linked mannosylation (GO: 0035269) and positive regulation of proteasomal protein catabolic processes (GO: 1901800). Studies have reported that the TMTC gene family is associated with hair colour [TMTC3, the SLC45A1 (Solute Carrier Family 45 Member 1) gene, which encodes a protein from the glycoside-pentoside-hexuronide cation symporter transporter family, was identified as a candidate gene during the association of staple length at 8 months; notably, this gene has also been associated with hair colour in humans [TMTC3 and SLC45A1 genes are involved in the mechanisms that affect colour, formation  and metabolism of hair and feathers in mammals and poultry.r colour ,48. MoreTRIM2, MND1, TLR2, RNF175, CEP290, TMTC3, RERE, SLC45A1, SOX2, MORN1, SKI, FAAP20, PRKCZ, GABRD, CFAP74, CALML6 and TMEM52. Of specific interest are CEP290, PRKCZ, TMTC3 and SLC45A1 genes, which have a strong association to molecular and biological processes involved in the hair follicle development of sheep. This study reveals certain functional information about the genetics of wool quality and production traits. The current study promotes future plans to increase wool quality and yield within the Akkaraman breed through the design of efficient breeding programmes. However, more studies are required to fully understand the genetics of wool traits. This study contained a relatively low sample size, especially when considering the number of measurements in yearling animals. It is important for the improvement of the breed to carry out future studies on the genetics of these traits with higher sample sizes and with increased wool quality and production traits.In the present study, heritability estimates of four wool qualities and one wool production trait were found to be moderate to high. Subsequent GWAS identified one genome-wide and six chromosome-wide significant SNPs for wool diameter and length traits. Unfortunately, there were not any significant SNPs found for yearling greasy fleece weight. Within significant SNPs, there were several genes suggested as candidates for further analysis; accordingly, these were