Here we report the results of the first systematic investigation of genetic and environmental influences on 57 psychological traits covering major issues in emerging adulthood such as aspirations, thoughts and attitudes, relationships and personality. We also investigate how these traits relate to physical and mental health, educational attainment and wellbeing.We use a sample of nearly 5000 pairs of UK twins aged 21–25 from the Twins Early Development Study. We included 57 measures of traits selected to represent issues in emerging adulthood (EA) such as aspirations, thoughts and attitudes, life events, relationships, sexual and health behaviour and personality. We also included measures related to what are often considered to be the core functional outcomes even though here we refer to the data collected at the same time: adverse physical health, adverse mental health, wellbeing, and education.All 57 traits showed significant genetic influence, with an average heritability of 34% (SNP heritability ~10%). Most of the variance (59% on average) was explained by non‐shared environmental influences. These diverse traits were associated with mental health (average correlation 0.20), wellbeing (0.16), physical health (0.12) and educational attainment (0.06). Shared genetic factors explained the majority of these correlations (~50%). Together, these emerging adulthood traits explained on average 30% of variance in the outcomes (range = 8% to 69%), suggesting that these traits relate to the outcomes additively.We conclude that even as the majority of individual differences in EA traits is explained by non‐shared environmental factors, genetic influence on these traits is still substantial; the environmental uncertainties of emerging adulthood in the 21st century do not diminish the importance of genetics. As adolescents travel down long and winding roads to adulthood, their trip is substantially influenced by genetic proclivities that nudge them down different paths leading to different destinations. Univariate twin analyses of additive genetic (A), shared environmental (C), and non‐shared environmental (E) components of variance for emerging adulthood traits. In the 21st century, emerging adulthood has stretched from the late teens through the twentiesThe first systematic investigation of genetic and environmental influences on 57 psychological traits covering major issues in emerging adulthood showed an average heritability of 34%. Most of the variance (59% on average) was explained by non‐shared environmental influencesThese diverse traits were associated with mental health (average correlation 0.20), wellbeing (0.16), physical health (0.12), and educational attainment (0.06). Shared genetic factors explained the majority of these correlations (∼50%)Together, these emerging adulthood traits explained on average 30% of variance in the outcomes (range = 8 to 69%), suggesting that these traits relate to the outcomes additivelyemerging adulthood. The transition from adolescence to adulthood is marked by instability, self‐focus, feeling in‐between, exploring identity in work and love, and an optimistic sense of possibilities.The winding road to adulthood is the subtitle of the field‐defining book on emerging adulthood (EA) by Jeffrey Arnett Arnett, , 2015. TMore than 6000 papers, primarily in psychology and psychiatry, have investigated emerging adulthood. Most of these are normative, describing average changes that take place during the twenties Arnett, . An enorGenetic and environmental analyses of individual differences in emerging adulthood have been reported for domains that are assessed throughout the life course  and the proportion of individual differences explained by shared environmental factors will be modest (less than 10%); the rest of the variance will be explained by non‐shared environmental influences.Traits with higher twin heritability will also have higher heritability estimates calculated from DNA of unrelated individuals . SNP heritability will be significant and substantial for psychological traits (average SNP h2 around 10%). The gap between twin heritability and SNP heritability will be substantial; twin heritability will generally be roughly twice the SNP heritability.Phenotypic correlations between psychological traits and outcome measures  will be significant and moderate (average correlation ∼0.30).Genetic correlations between psychological traits and outcome measures  will be substantial (0.5–0.7). More than half of the phenotypic correlations between psychological traits and outcome measures  will be explained by genetic factors.There will be significant mean differences between males and females in some psychological traits with males showing more risk‐taking, anti‐social behaviour, alcohol and substance use compared to females; and females reporting more anxiety, depression and victimization, but also more volunteering. We do not expect large differences between males and females in key functional outcomes. While many of the mean differences will be significant given our large sample size, we predict that the effect sizes will be small with variance explained by gender less than 5%.There will be no differences between males and females in the aetiology of psychological traits in emerging adulthood, that is, we predict that there will be no significant qualitative or quantitative genetic differences between males and females.Although this study was an exploratory study due to the limited research on emerging adulthood, we made some general predictions based on the results of behavioural genetic research throughout the life course. These predictions, together with the analysis plan, were preregistered in Open Science Framework (osf.io/m6vwj) prior to accessing the data.Our sample was drawn from TEDS, a longitudinal study that recruited through national birth records over 16,000 twin pairs born between 1994 and 1996 in England and Wales. Although there has been some attrition, more than 8000 twin pairs are still engaged in the study, and TEDS remains reasonably representative of the population in England and Wales in terms of ethnicity and family socio‐economic factors, including data from the most recent data collection used in the present study , therefore, the sample size varied between measures. Sample size by zygosity for each measure is presented in Tables Here we used the subsample of twins who had contributed to the TEDS‐21 data collection. At the start of TEDS‐21 data collection, twin ages ranged from 20.56 to 25.59 years . All data were collected using questionnaires. Due to the length and number of measures included, data collection was completed in two phases . In both phases of TEDS‐21 questionnaire data collection, participants could complete the assessment via a smartphone app, via the web, or on paper. Incentives included a £10 voucher on completion of each phase. Twins were also offered entries in prize draws at intervals during the data collections of phases 1 and 2. Ethical approval was provided by the King's College London Research Ethics Committee (reference number: PNM/09/10‐104) and informed written consent was received from all participants. No exclusions were applied. All available data were used  Summary of emerging adulthood measures. (B) Summary of functional outcomes; physical and mental health, wellbeing, achievement and planned educational level. Outer ring includes factor loadings to First Principal Component (PC1) for each outcome; third ring shows the % of variance explained by PC1 across measuresDNA samples have been obtained from 12,500 individuals and genotyped on one of two DNA microarrays (Affymetrix GeneChip 6.0 or Illumina HumanOmniExpressExome chips). After stringent quality control, the total sample size available for genomic analyses is 10,346 . Of these, 7289 individuals were genotyped on Illumina arrays, and 3057 individuals were genotyped on Affymetrix arrays  included the identification of random responders, or “clickers”. Clickers were identified from a combination of the following: incorrect responses on a quality control item , too rapid responding , or too uniform responding . Typically, around 1% of twins were excluded per questionnaire section but this varied from 0.3% to 5.3% (and of course 0% in themes without any measure containing a QC item). Twin data were excluded for an entire questionnaire section if the twin was identified as a clicker for any measure within that theme. Furthermore, twin data were excluded for an entire questionnaire (phase 1 or phase 2) if the twin was identified as a clicker for two or more themes within that questionnaire. For further details, including percentages of quality control exclusions, see Appendix Data cleaning . Scripts have been made available on the OSF site. All analyses were completed using SPSS Statistics software  and R version 4.0  was used to test for mean differences for sex and zygosity and their interaction. Because significant, though small, sex differences emerged Table , we corrWe created standardised composite scores representing adverse mental health, adverse physical health and wellbeing using PCA. We obtained the first principal component (first PC) of behaviour problem/mental health phenotypes  for the whole sample. We then repeated the analyses separately for males and females.R2 values.We also conducted multiple regression to assess how much variance in the functional outcomes could be explained in total by the 57 traits, reporting the adjusted A), shared environmental (C), and non‐shared environmental (E) effects on the variance and covariance of phenotypes, by comparing intraclass correlations for MZ and DZ twins . Non‐additive genetic variants can be detected if the DZ correlation is less than half the correlation between MZ twins . This method capitalises on the fact that identical  twin pairs are genetically identical and share 100% of their genes, while non‐identical  twin pairs share on average 50% of their segregating genes. Shared environment is defined as aspects of the environment that make members of twin pairs similar to one another. Non‐shared environmental influences are unique to individuals and do not contribute to similarities between twins. Using these family relatedness coefficients, it is possible to estimate the relative influence of additive genetic , indicating the extent to which the same genetic variants influence two phenotypes independent of their heritabilities. The shared environmental correlation (rC) and non‐shared environmental correlation (rE) can also be estimated . We selected the lifetime item to maximise power (lifetime N = 4727 vs. last year N = 3887).As indicated in Figure Figure Figure All EA traits as well as functional outcomes showed significant heritability as presented in Figure A) was modest on average . The most heritable trait in emerging adulthood is risky sexual behaviour . Other traits with substantial heritability include general risk‐taking behaviour , media use , health behaviours , environmental concerns  and quality of relationship with mother . These heritabilities are higher than those for the Big Five personality traits, which range from 0.22 (95% CI: 0.18–0.27) to 0.40 (95% CI:.36–0.44). For the outcome variables, the adverse mental health composite is more heritable  than the adverse physical health composite .Across EA traits and functional outcomes, heritability  were modest on average , but for a few variables substantial effects were observed. The greatest shared environmental effect was found for communication with father . Substantial shared environmental influence was also found for contact with father  and quality of relationship with father . Two other traits showing substantial shared environmental effects were relationship with twin  and religiosity .E) factors that accounted for 59% of the variance on average (E range 24%–88%). The largest non‐shared environmental effects were observed for physical and social peer perpetration . Importantly, this component of variance also includes measurement error.The majority of the individual differences were explained by non‐shared environment , shared environmental (C) and non‐shared environmental factors are indicated by the red, dark blue and light blue bars, respectively(A) Genetic correlations between EA traits and outcomes derived from bivariate twin analyses; (B) phenotypic correlations between EA traits and key outcomes . Proportion of each correlation explained by additive genetic (r = −0.23) and positively correlated with adverse mental health (0.50) and adverse physical health (0.38). These three correlations are largely mediated genetically—56%, 54% and 60%, respectively. The rest of these correlations are mediated by nonshared environmental factors. For all of the substantial phenotypic correlations between EA traits and outcomes, genetic factors account for the majority of the correlations. An equally important finding is that a large proportion of the correlations between EA traits and functional outcomes is explained by non‐shared environmental factors. For example, daily hassles correlated with wellbeing (r = −0.31), these are almost in equal parts mediated by genetic and non‐shared environmental factors . Shared environment does not significantly contribute to any of these correlations.On average, more than 50% of these phenotypic correlations were mediated by genetic factors, 60% for mental health, 56% for physical health, 50% for wellbeing and 52% for the two education outcomes. For example, the first row of Figure Genetic contributions to the phenotypic correlations are based on the genetic correlation weighted by the heritability of the two traits. The genetic correlation estimates the extent to which the same genetic variants influence the two traits, regardless of their heritabilities. Figure The shared environmental correlation (rC) and non‐shared environmental correlation (rE) can also be estimated. The shared environmental correlations are illustrated in Figure Although the sample size with genotyped DNA limited power to detect SNP heritability, for completeness we present these estimates in Table Univariate twin and GREML analyses reported above were repeated with van der Waerden transformed variables. The results of these analyses are presented in the Supporting Information Societal changes have stretched the transition from adolescence to adulthood, which has created a new developmental period called emerging adulthood. The prolonged uncertainties about identity, relationships and occupations can create stress but they also offer flexibility and unprecedented opportunities for young people to find their adult selves. These opportunities are amplified by the Internet and social media for this first generation of digital natives.Here we reported the results of the first systematic investigation of genetic and environmental influences on 57 psychological traits covering major issues in emerging adulthood such as aspirations, thoughts and attitudes, relationships and personality. All 57 traits showed significant genetic influence, with an average heritability of 34% while the majority of individual differences (59% on average) were explained by non‐shared environmental influences. These diverse traits were associated with mental health (average correlation 0.20), wellbeing (0.16), physical health (0.12) and educational attainment (0.06). Shared genetic factors explained the majority of these correlations (∼50%). Together, these emerging adulthood traits explained on average 30% of variance in the outcomes (range = 8%–69%), suggesting that these traits relate to the outcomes additively.As mentioned in the Introduction, the ever‐expanding menu of idiosyncratic experiences for self‐exploration during emerging adulthood suggests that environmental influence is likely to be critical for emerging adulthood traits. The present results from this first systematic analysis of emerging adulthood traits in a genetically sensitive design are consistent with this hypothesis in that the environment is the major source of individual differences in EA traits. Moreover, more than 90% of the environmental influence is due to non‐shared environment, which includes measurement error. Non‐shared environment could reflect idiosyncratic experiences in the chaotic world of emerging adulthood, experiences not likely to be shared by siblings. This non‐shared environment is especially relevant when examining the differences between MZ twin pairs, who share their genes and home environment, are the same age and same sex. Shared environmental influences account for only 5% of the variance of the 57 EA traits on average. In other words, most environmental effects on EA traits are not shared by siblings, even twins, who grow up in the same family. As siblings leave the family home in early adulthood, it seems likely that they each make their own independent way in the world, having trait‐relevant experiences not shared with their sibling. Part of our ongoing research is to investigate the environmental experiences that account for individual differences in emerging adulthood using the longitudinal data collected in TEDS.Another possibility is that the heterogeneity of experience in early adulthood provides an opportunity for genetic potential to be realised as individuals select, modify and create environments correlated with their genetic propensities. This is gene–environment correlation, which contributes to heritability. In support of this hypothesis, we found significant genetic influence for all 57 EA traits, many of which have not been investigated previously in emerging adulthood. On average, genetic influences accounted for 34% of the variance. Moreover, the most highly heritable traits in our study are traits that are especially relevant to emerging adulthood: risky sexual behaviour (60% heritability), general risk‐taking behaviour (50%), media use (51%) and concerns about the environment (53%). An additional possibility is gene–environment interactions, in which individuals' reactions to environmental influences depend on genetic factors.Although our results can be viewed as providing support for both hypotheses, it is also possible to interpret the results as support for neither hypothesis because both moderate genetic influence and substantial non‐shared environmental influence are found for nearly all traits throughout development. These findings are consistent with meta‐anayses of over 50 years of twin studies showing that the majority of the twin resemblance is due to genetic factors, with the average heritability of all traits around 50%, and the rest of the variance is explained by non‐shared environmental factors, not shared by twins growing up in the same family  for traits most relevant to emerging adulthood and the evidence for genetic change during emerging adulthood leads us to predict that the heritability of EA traits will increase as we follow the twins into established adulthood. In any case, it seems that the turmoil of emerging adulthood does not shuffle the deck of individual differences. One firm conclusion is that heritability is not swamped by the environmental heterogeneity of emerging adulthood.Our results also indicate that EA traits are associated with wellbeing, adverse mental and adverse physical health and educational outcomes. Although the associations were modest to moderate for individual EA traits and outcomes, the EA traits acting additively and together accounted for 31% of the variance in the outcomes on average. We show that adverse mental and physical health, wellbeing and educational attainment reflect a package of heritable EA traits. These findings also indicate that when studying individual differences in health, wellbeing or socioeconomic phenotypes in emerging adulthood, diverse traits should be considered such as life goals and aspirations and relationships. The additional implication of the results points to genetic confounding when studying the links between these functional outcomes and diverse psychological traits, as our results from bivariate genetic analyses show that these correlations are mediated mostly by genetic factors. Future research should use genetically sensitive designs when studying the correlates of functional life outcomes. However, these findings underscore the adage that correlation does not imply causation. For example, perceived daily hassles correlates negatively with wellbeing and positively with adverse mental and adverse physical health, but it cannot be assumed that daily hassles cause wellbeing or health.Compared to health and wellbeing outcomes, we explained much less variance in educational outcomes , compared to our previous reports in earlier childhood or even late adolescence  and informed written consent was received from all participants. No exclusions were applied.Supporting Information S1Click here for additional data file.


Linkage disequilibrium (LD) decay was rapidly declining with increasing physical distance across all chromosomes. The initial 50% LD decay was ~ 5 Kb and background level was within ~ 80 Kb. This study detected 42 significant quantitative trait nucleotide (QTNs) for different traits evaluated using FarmCPU, SUPER and 3VmrMLM which were in proximity with candidate genes related and were co-localized in already reported quantitative trait loci (QTL) and phenotypic variance (R2) of these QTNs ranged from 3 to 20%. Haplotype validation of the candidate genes from this study resulted nine genes showing significant phenotypic difference between different haplotypes. Three novel candidate genes associated with agronomic traits were validated including Sobic.001G499000, a potassium channel tetramerization domain protein for plant height, Sobic.010G186600, a nucleoporin-related gene for dry biomass, and Sobic.002G022600 encoding AP2-like ethylene-responsive transcription factor for plant yield. Several other candidate genes were validated and associated with different root and physiological traits including Sobic.005G104100, peroxidase 13-related gene with root length, Sobic.010G043300, homologous to Traes_5BL_8D494D60C, encoding inhibitor of apoptosis with iWUE, and Sobic.010G125500, encoding zinc finger, C3HC4 type domain with Abaxial stomatal density. In this study, 3VmrMLM was more powerful than FarmCPU and SUPER for detecting QTNs and having more breeding value indicating its reliable output for validation. This study justified that the constructed subset of diverse sorghums can be used as a panel for mapping other key traits to accelerate molecular breeding in sorghum.This genome-wide association studies (GWAS) used a subset of 96 diverse sorghum accessions, constructed from a large collection of 219 accessions for mining novel genetic loci linked to major agronomic, root morphological and physiological traits. The subset yielded 43,452 high quality single nucleotide polymorphic (SNP) markers exhibiting high allelic diversity. Population stratification showed distinct separation between  Furthermore, declining availability of required fertile land for expanding under cereal crops and challenges due to changing climatic conditions, make it hard to meet global food demand with prevailing agricultural practices2. Due to their adaptation to extreme weather events, requirement of less inputs and possession of high nutritional values and therapeutic clues, climate resilient cereal crops are becoming ideal crops for improving global food security4. Most of the crop improvement programs in these hardy cereals were focused on easy to phenotype traits, such as grain yield, nutritional contents, plant architecture, disease, and insect resistance. However, breeding for abiotic stress tolerance had limited success due to difficulties in the phenotyping of stress-associated traits viz., root morphology, stomatal traits, and gas exchange parameters.Cereal crops provide over 75% of human calories and projected increase in the global population necessitates 70–100% increase in cereal food production by 2050Sorghum bicolor (L.) Moench] is the world's fifth most important cereal crop in terms of total production and acreage with 40.25 million ha under cultivation and 58.7 million MT of grain production5. Sorghum is grown for a variety of purposes in areas with low or erratic precipitation, including grain for food or livestock feed, forage biomass for livestock feed, and sweet sorghum for syrup and biofuel production6. Sorghum exhibits high genetic diversity where ~ 236,617 germplasm accessions including cultivated and weedy relatives are conserved in gene banks around the world7. However, utilization of these large germplasm collections in crop improvement program is extremely limited. It is primarily due to the photosensitivity, and non-availability of reliable data on the genetic variation for complex traits of economic interest as well as other factors such as linkage load of undesirable genes, restricted germplasm access, and international exchange regulations8. Given the availability of pan-genomes and genetic resources for sorghum improvement9, it is important to investigate and understand the genetic basis for different complex traits like root architecture and physiological traits that serve as the foundation for improving abiotic stress tolerance in sorghum, particularly drought stress.Sorghum [10. RSA provides the basis for stress tolerance, have been studied extensively to dissect its genetic basis in Arabidopsis13 while limited studies have been attempted in sorghum15.Root traits are critical for water and nutrient uptake, climate resilience and productivity, but receive little attention due to phenotyping difficulties. Root system architecture (RSA), determined by primary and lateral root length, number, spread, and biomass, which exhibits greater plasticity in response to environmental shifts, could be critical in developing drought hardy genotypes. Understanding the genetic and molecular basis of RSA may help to improve resource use efficiency and to break any linkage between yield and stress tolerance as in the case of rice16. Stomata plays a vital role in determining transpiration rate (TR) and thus it holds a promising role in maintaining water balance, turgor pressure, nutrient uptake and tolerance to drought and high temperatures. Genetic manipulation of TR and water use efficiency (WUE) has been demonstrated by altering stomatal development, and density in various monocots and dicots17. Decrease in stomatal density was found to increase drought tolerance and intrinsic WUE (iWUE) without affecting the yield in cereals18. Despite of its greater role in maintaining water balance in plants, only limited studies have been attempted to unravel genetic loci governing stomatal traits in sorghum19. The majority of genome wide association studies (GWAS) presented in sorghum were conducted with sorghum conversion program germplasm, which limited its success in effectively dissecting genetic loci affecting the traits of interest21.Stomatal traits play an important role in regulating crop productivity and stress tolerance under diverse environmental conditions22. Several GWAS employ the genotyping-by-sequencing (GBS) strategy to generate dense single-nucleotide polymorphism (SNP) markers covering the whole genome23. Several models are available for performing GWAS, from which models like settlement of MLM under progressively exclusive relationship (SUPER) and fixed and random model circulating probability unification (FarmCPU) have shown to be more common models and 3 variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM) is with high reliable accuracy. The model SUPER is a single locus model which uses bin approach to select associated markers24 and the model FarmCPU is a hybrid model which uses both fixed and random effect model decreasing the results with less false positive and negative making it more robust25. The model 3VmrMLM have shown to cover quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) with higher power and accuracy in QTN detection particularly in small mapping population26. Although several attempts were made to dissect genetic loci and candidate genes for various agronomic traits through GWAS29, there still remains a gap for understanding the genetic basis of several abiotic stress tolerance related root and physiological traits which are important and critical to make sorghum as a more climate-resilient crop. This study assembled a representative subset of diverse sorghum lines constructed from a larger collection of sorghum germplasm to effectively identify major genetic loci and novel candidate genes for major agronomic, root morphological and physiological traits through GWAS. In addition to identification of significant SNPs, the genetic effect of these SNPs with significant associations to their respective traits were determined from its phenotype in this study for validation. Several studies have validated their SNPs by understanding their genetic effect with the traits’ phenotypes32. Further, extensive bioinformatics studies were carried out to identify causative gene(s) underlying genetic variation for grain color, plant height and few major physiological traits. Overall, this pilot scale initiative paved way for an extensive GWAS analysis for mapping genetic loci/genes underlying major agronomic and climate resilience traits. The specific objectives were to: (1) construct a representative subset from a large collection of sorghum germplasm accessions using simple sequence repeat (SSR) microsatellite markers, (2) sequence the constructed subset germplasm accessions to understand the allelic diversity and population structure, and (3) perform GWAS using three models  for agronomic, root and physiological traits and identify the novel quantitative trait loci (QTL) and identify candidate genes associated with these traits to validate the study.Bi-parental mapping populations were commonly used to identify genomic regions underpinning traits of interest in major crops. However, the success rate is limited due to the narrow allelic diversity and low genomic resolution which limited mapping candidate genes underlying traits of interest. With advances in high-throughput genotyping using next generation sequencing (NGS), use of diversified association mapping panels in gene discovery has become popular due to its ability to overcome the significant limitations of bi-parental populationscaudatum and durra with about 60 accessions having ancestry proportion ≥ 0.8 assigned to each sub-population, and at K = 3, distinct separation of guinea race was also observed along with durra and caudatum. Linkage Disequilibrium (LD) among the filtered SNPs (r2) used in the subset were rapidly declining with increasing physical distance for all chromosome, with the initial 50% decay by ~ 5 Kb and decay to the background level (r2 < 0.1) within ~ 80 Kb  ranging between 0.8 and 0.97. Root morphological and stomatal traits showed medium to high heritability (0.53–0.92). Moderate-to-high heritability exhibited by all the ten traits enabled detection of candidate genes using GWAS  of 3%. Another non-synonymous QEI (S10_52722710) was found to be significantly associated with dry biomass using 3VmrMLM in Sobic.010G186600, a nucleoporin-related gene showing R2 of 3%. Seven QEIs by 3VmrMLM and six QTNs by FarmCPU were found to be significantly associated with single plant yield and present in proximity (within 50 kb) with the candidate genes related to panicle development.The method 3VmrMLM detected a non-synonymous QEI (S1_76855452) significantly associated with plant height present in Sobic.001G499000, encoding potassium channel tetramerization domain showing phenotypic variance  whereas, 3VmrMLM detected six significant QTNs Fig. . There w2 and is present in Sobic.010G125500, encoding zinc finger, C3HC4 type domain. For iWUE, S10_3353158 was detected as a significant QTN present in Sobic.010G043300, which has homologous function in wheat encoding inhibitor of apoptosis 1 showing 11% R2.The methods FarmCPU and SUPER did not detect significant QTNs for all other physiological traits evaluated. Whereas, a total of six significant QTNs  were detected by using the method 3VmrMLM  between different haplotypes  non-synonymous SNPs with MAF > 0.05 per candidate genes were used for haplotype analysis. From the haplotype analysis, nine out of 42 candidate genes were validated showing significant difference . However, productivity enhancement under stress conditions is a challenging task, because of the unpredictable nature of most periods of water and temperature stresses in sorghum growing areas and gaps in our knowledge of abiotic stress biology. In this critical situation, it is of paramount importance to focus on various biotic and abiotic stress-tolerant key traits with more emphasis of following accelerated classical and genomics-assisted breeding approaches. Uncovering QTL with significant phenotypic variation through GWAS analysis on challenging polygenic traits would help to expedite traits introgression and marker assisted selection (MAS). GWAS studies for QTL mapping use the phenotypic field data from multi-environments or controlled environment(s) to increase the mapping resolution36. Parra-Londono et al.33 performed GWAS using phenotypic data from one controlled greenhouse study for contrasting phosphorus availability and identified several candidate genes for root architecture traits by integrating the genotypic data in sorghum. Mandozai et al.34 performed GWAS in soybean on seedling root and shoot related traits and identified 27 significant SNPs with high phenotypic variance using the phenotypic data from one controlled environment study. Chen et al.35 conducted one greenhouse hydroponic study in soybean on recombinant inbred lines (RILs), mapped several QTL and detected qRL16.1, a novel QTL influencing primary root length. In cowpea, Wu, et al.36 performed GWAS and identified several candidate genes associated with stomatal closure from one controlled environment study.Sorghum is resilient to biotic and abiotic stresses and its wider adaptability with low input requirements, high nutritional and food value continues to play a vital role in global food security37. Limited studies were conducted to dissect genomic regions responsible for root morphological and physiological traits38. Parra-Londono, et al.33 evaluated RSA under different phosphorus availability in one greenhouse environment using germination papers and bioassay plates. Similar studies have been attempted in other crops for GWAS on RSA at vegetative stage in a controlled environment34. The current study attempted to better utilize the germplasm resource by constructing a subset of 96 accessions and performed GWAS to co-localize the previously reported and new novel loci and candidate genes associated with agronomic, root morphological, and physiological traits.In sorghum, several studies were attempted to explore genetic loci affecting yield and stress tolerance through GWAS8. This study initiated with the construction of a subset consisting of 96 diverse accessions representing the genetic and geographical diversity of 219 accessions which is more rewarding for enhanced utilization in breeding programs8. The wide coverage of genetic diversity in the constructed subset is clearly justified by the following different analyses: (i) p-value > 0.05 for diversity parameters of three agronomic traits performed in the subset showed no significant difference with the whole collection  observed at ~ 80 Kb -by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) and almost unbiasedly estimate their effects, with high accuracy and power with a low false positive rate even in small mapping population26. In this study, GWAS was performed for all nine traits using FarmCPU, SUPER and 3VmrMLM, methods which detected 42 candidate genes together for all the traits. Haplotypes are a unique combination of jointly inherited SNPs/indels in the same chromosome segment, so haplotype analysis are a useful method for overcoming the biallelic constraints of SNPs and increasing the allelic resolution of candidate genomic areas43. Considering this, validation of the candidate genes identified in this study were performed by haplotype analysis which validated nine candidate genes showing significant phenotypic variance. Only the non-synonymous SNPs from candidate gene were considered for haplotype analysis as change in amino acid change in gene are caused by non-synonymous SNPs.Several statistical methods are available to perform GWAS from which few have high accuracy and power in mapping loci. SUPER method use single locus model for mapping locus and real data has demonstrated that SUPER is superior and powerful than regular mixed linear model (MLM)44. Several QEIs and QTNs detected for agronomic traits were co-localized in already reported and characterized QTLs indicating the efficiency of the subset used in this study that can be used for mapping any traits of interest. Sorghum has four key dwarfing loci (dw1- dw4) that affect plant height, of which dw1, dw2, and dw3 have been characterized47. In this study, a QEI (S6_44429741) was detected to be associated with plant height which was colocalized in dw2 locus (21.2 Mb–44.99 Mb) and this QEI was found to be ~ 1.6 Mb away from the causal gene Sobic.006G067600, a protein kinase in dw2 locus controlling height. S9_56525300, a QTN detected to be associated with dry biomass by FarmCPU was co-localized in dw1 locus (53.89 Mb–57.86 Mb) which regulate stem internode length in sorghum. However, this QTN was ~ 513 kb away from Sobic.009G229800, the causal gene for dw1 locus.This study detected QTNs and QEIs for agronomic traits, as they were evaluated for two field environments. As global climate change increases, QEIs become increasingly important in the genetic dissection of complex quantitative traits in crops48 where a non-synonymous QTN (S1_76855452) was detected. Haplotype analysis was performed to validate further by using two non-synonymous SNPs in Sobic.001G499000 revealing a significant phenotypic difference in plant height between the four haplotypes formed , a reported QTL for plant heightmed Fig. . S10_527ass Fig. .51. Previous studies have shown that qTGW1a (Sobic.001G341700) encoding G-protein γ subunit located at the N-terminus are responsible for grain weight in sorghum52. Multi-seeded (MSD) genes including MSD1 (Sobic.007G021140)53, MSD2 (Sobic.004G078600)54, and MSD3 (Sobic.001G407600)55 have reported to regulate grain number per panicle in sorghum. This study did not detect any QEIs or QTNs in proximity with the functionally characterized genes. However, some detected QTNs were co-localized in already reported QTLs increasing the potential of the candidate genes at the QTN proximity to affect plant yield. Sobic.002G022600 encoding AP2-like ethylene-responsive transcription factor AINTEGUMENTA was identified as a candidate gene which was present in proximity with S2_2070779, a significant QTN detected to be associated with plant yield by FarmCPU. This gene function has been reported to affect inflorescence branching and kernel number in maize indicating the possibility of the detected candidate gene to affect yield56. This candidate gene is present in qtl2bGWP, an already reported QTL57 for panicle weight and resulted in four haplotypes showing significant phenotypic difference validating its possibility to affect plant yield  Sobic.005G104100, peroxidase 13-related gene. Peroxidase related genes have shown to promote root hair growth at low temperature stress in Arabidopsis which increases the chance of the candidate gene detected to be associated with root length in this study60. Further validation of this candidate gene showed significant difference between three haplotypes formed  associated with iWUE in proximity (~ 213 kb) with SbWRKY30 (Sobic.010G045700) and is present in QLpt.txs-G, a QTL affecting leaf morphology64. Yang, et al. 63 showed that Arabidopsis and rice plants benefit considerably from heterologous expression of SbWRKY30 and also it directly activate the drought stress-responsive gene SbRD19 in sorghum. The detected QTN in proximity with SbWRKY30 from this study is a non-synonymous SNP present in Sobic.010G043300, homologous to Traes_5BL_8D494D60C, encoding inhibitor of apoptosis. Several studies have shown that anti-apoptosis genes regulate and improve stress tolerance in Arabidopsis and rice65. This candidate gene showed five haplotypes with significant difference between them further validated its potential to be associated with iWUE  at the whole plant level. Genes including 17. ERECTA, leucine-rich repeat receptor-like kinase gene in Arabidopsis is known to regulate stomatal density, and patterning66. In rice, OsSRO1c plays a dual role in drought tolerance by inducing stomatal closure and H2O2 accumulation67. There are no reported functionally characterized genes for stomatal density (SD) and development in sorghum. This study detected a significant non-synonymous QTN (S10_15510030), co-localized in Sobic.010G125500, encoding zinc finger, C3HC4 type domain and present in LA3, a QTL responsible for drought tolerance49. Zinc finger protein have shown to mitigate abiotic stress tolerance via stomatal aperture control in rice which increases the chance for the detected candidate gene to be associated with SD68. Sobic.010G125500 showed significant difference between three haplotypes formed validating its potential to be associated with SD  ranging from 3 to 20%. Results from this study indicates that, 3VmrMLM is the best choice for mapping any trait of interest in particular with small mapping population. FarmCPU and SUPER did not detect any QTN associated with root and physiological traits while 3VmrMLM detected in this study. The validation of nine candidate genes associated with different traits using haplotype analysis which were co-localized with the previously reported QTL  to high heritability (97% for plant height) indicating high additive fixable genetic effects of these traits. This study attempted to detect QEIs for agronomic traits and QTNs for agronomic, root morphological and physiological traits using three different models including FarmCPU, SUPER and 3VmrMLM. The detected 42 QTNs in this study have potential in sorghum breeding as the QTNs had high phenotypic variance  and differing widely in their grain color was obtained from the National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India  reps after burning and K assumed population value was set from 1 to 10 with 10 iterations. Optimum K value was fixed by using STRUCTURE harvester72. A representative subset was constructed based on the advanced M strategy using heuristic search available with PowerCore73. To validate the representativeness of the constructed subset, student’s t test for Nei’s genetic distance and Shannon’s information index, and one way ANOVA and Levene’s test for variance were performed for three major agronomic traits recorded during Rabi’2019 for the entire 219 lines and the subset of 96 accessions74.Genotyping of 219 sorghum accessions using 17 SSR markers generated in our earlier study75.Data on three major agronomic traits including plant height (PH), single plant yield (SPY) and dry biomass (DB) recorded in Rabi 2019 and Rabi 2020 was used. Three plants per accession in each replication were selected for recording the data on these traits by following the sorghum descriptors2) each with one seed. PVC pipes were filled with soil mixture consisting two parts of coir pith and one part of soil at the 45th day from planting for all replicates of accessions to maintain homogeneity.Root morphological traits like root length (RL) in cm, root thickness (RT) in mm, and dry root weight (DRW) in g, were measured during the vegetative stage in all the 96 accessions grown under Greenhouse conditions at Tamil Nadu Agricultural University, Coimbatore, India . Three replicates of each accession were planted in individual Polyvinyl chloride (PVC) pipes (90 × 10 cm2 m−2 s−1), stomatal conductance  and transpiration rate (TR)  were measured in 45 days old plants in the second leaf from the top using LCi-SD Ultra Compact Photosynthesis System . Intrinsic water use efficiency (iWUE) was calculated by dividing photosynthesis rate by stomatal conductance (gs). All these measurements were recorded between 10.00 am and 12.00 noon (IST) with five observations at the second leaf in each replication to maintain homogeneity.Stomatal traits  and gas exchange parameters were measured in all the 96 accessions grown under greenhouse conditions as described above. Accessions were raised in pots with soil mixture (1:1:1 soil: coir pith: vermicompost) with two replications per entry. Gas exchange parameters viz., photosynthesis rate  were taken from abaxial leaf surfaces using cellophane tape and the same were placed on the microscopic slide (75 × 25 mm2) and viewed at 200X magnification using 20X NIS60 planar lens (20X/0.5ph) on RXLr-5 (NEXcope) fluorescent microscope having Jenoptik optical system with ProgRes® capture camera control system (v 2.10.0.1). Stomatal images were captured in jpeg file format at the highest resolution (2580 × 1944 HQ) with microscopic view area of 0.27 mm2. Five microscopic images were recorded in all replications on abaxial leaf surfaces, with a total of 960 images (96 accessions x two replications x five images) to estimate SD (mm2), SCA (µm-2) and SCTA (µm-2). SD was calculated separately by manually counting the total number of stomata in the field of view divided by 0.27 mm2. SCA was calculated by ProgRes® from five randomly selected stomata of each image, SCTA was calculated by multiplying the total number of stomata by its respective SCA in each image. Frequency distribution was performed for these traits using Minitab 1977 and heritability was performed using metan, an R package78.Stomatal imprints of abaxial leaf surface were taken from the second leaf from the top using nail polish and used for measuring stomatal density (SD), stomatal complex area (SCA) and stomatal complex total area (SCTA)79 and high-quality DNA was used for GBS analysis. The assessment of DNA quality and quality was done by running on 1% agarose gel. GBS libraries were prepared using the protocol adapted from Elshire, et al. 23. Type II restriction endonuclease (ApeKI) was used for DNA digestion and ligated to the adapter, followed by 96-plex library was constructed as per GBS protocol. Sequencing was carried out using HiSeq X sequencing platform .Genomic DNA was extracted from 40-day old leaves of all the 96 accessions using CTAB method80 using Burrows-Wheeler Alignment (BWA) software81, while reads carrying “N” had been removed to perform further analysis. The perfectly matched and aligned sequences were processed further for SNPs calling and genotyping through freebAYES pipeline. SNPs filtering was done using TASSEL 5.082, where SNPs with missing rate > 20%, minor allele frequency (MAF) < 0.05 and Heterozygosity > 0.1 were excluded, and further missing SNPs were imputed using Beagle imputation 5.083. A total of 43,452 robust SNPs were retained for GWAS. Density of the filtered SNPs on the sorghum genome was visualized using CM plot, an open R package84.Sequence reads (FASTQ files) were processed and analyzed for SNP detection using \an in-house pipeline. Pipeline allows searching of all raw sequencing reads with perfectly matched barcode and expected remnant bp of restriction cut site and reads were further sorted, de-multiplexed and trimmed by using Trimgalore to create a unique, 150-bp long sequences called tags. These good quality tag sequences were aligned with BTx623 sorghum reference genome20. First, common SNPs with 0.01 MAF and 80% missingness from the constructed subset and 401 global sorghum accessions were filtered and retained. Next, PCA was performed where two PCA axes were built with previously published ApeKI GBS data of 401 global sorghum accessions20, and the constructed subset were projected on these axes using prcomp and predict function in R85. LD decay of the SAP and the subset was calculated and plotted using ~ 100 K SNPs and ~ 10 K SNPs respectively, where parameters were set for MaxDist 500 kb and 0.05 MAF. Detailed description of PCA and LD decay analyses were described in Marla, et al.86. Model-based maximum likelihood approach implemented by ADMIXTURE v1.2387 were performed for inferring population structure of the constructed subset using 4014 SNPs retained after thinning with 0.05 MAF from TASSEL 5.0.Population structure of the subset was inferred using principal component analysis and model-based maximum likelihood approach. Linkage disequilibrium (LD) and principal component analysis (PCA) for the constructed subset was characterized and aligned with sorghum association panel (SAP)88. Significant QTNs exceeding Bonferroni correction threshold (5.9) from all traits were compared against known QTL for the related traits and candidate genes within/near significant SNPs (~ 50 kb) were searched using Sorghum QTL Atlas89 and Phytozome v.13. Haplotype analysis was performed for all the identified candidate genes using Haploview 4.290 but only non-synonymous SNPs in the candidate genes were considered for haplotyping. Duncan analysis was performed to test statistical significance to understand phenotypic performance of each haplotype.Phenotypic data of all the agronomic, root and physiological traits was integrated against the genotyping data of 43,452 robust SNPs to perform GWAS for identifying QTNs) linked with all nine  traits evaluated, and QEIs associated with three agronomic  traits. Three methods FarmCPU, SUPER, and 3VmrMLM were used to perform GWAS on all the nine traits. These three methods were performed using genome association and prediction integrated tool (GAPIT v3.0) and 3VmrMLM available in the R statistical packageAll methods in this paper were carried out in accordance with relevant guidelines in the method section.Supplementary Information 1.Supplementary Information 2.


Egg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average.Estimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn . Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age.The heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.The online version contains supplementary material available at 10.1186/s12711-023-00862-7. Heterosis refers to the phenomenon that performance of hybrids is superior to the average performance of their parental breeds or lines. Heterosis is reported to be caused by non-additive genetic effects  and is wIndigenous Beijing-You chickens produce eggs with a higher yolk proportion and lecithin content than commercial layer breeds, such as White Leghorn , but theSeveral studies have estimated heterosis for egg weight , egg numSome studies have investigated differences between reciprocal crosses , 18, 19,In the present study, purebreds and their reciprocal crosses of Beijing-You and White Leghorn were bred and monitored for egg weights, egg production, and egg quality traits, to analyse the genetic background and estimate heterosis of longitudinal economically important egg laying traits throughout the laying period.The Beijing-You chickens (Y) used here were from a pedigreed pure line kept on the experimental farm of the Institute of Animal Science of the Chinese Academy of Agricultural Sciences. This line was selected for egg number and egg weight at a relatively low intensity for 18 generations. In the most recent five generations, about 60 roosters were mated with 500 hens, avoiding matings that would result in offspring with an inbreeding coefficient higher than 0.04. The other parental line, White Leghorn , was imported from the University of Guelph in 2018. The W chickens were kept on the same farm as the Y chickens for two generations using random mating. The Y and W chickens were kept in individual cages and fed ad libitum with a breeder diet containing 19% crude protein (CP), 2840 kcal/kg metabolizable energy (ME), 3.50% Ca, and 0.32% nonphytate P. The two breeds were mated to generate four genetic groups, using 30 Y and 30 W roosters, and 300 Y and 300 W hens: (1) 30 Y roosters with good sperm motility and sperm concentration were randomly mated with 150 Y hens to generate YY purebred offspring, (2) the same 30 Y roosters were randomly mated with 150 W hens to generate YW crossbred offspring, (3) 30 W roosters with good sperm motility and sperm concentration were randomly mated with 150 W hens to generate WW purebred offspring, and (4) the same 30 W roosters were randomly mated with 150 Y hens to generate WY crossbred offspring. In total, 1369, 1838, 1372, and 1836 eggs were collected for hatching for YY, WW, WY, and YW, respectively.In total, 487 YY, 675 WW, 507 WY, and 714 YW healthy female chicks were hatched simultaneously. They were vaccinated against Marek’s disease, infectious bursal, Newcastle disease, and infectious bronchitis, and transferred to the same brooding pen. At 18 weeks of age, 315 YY, 271 WW, 315 WY, and 359 YW chickens were randomly selected from all available chickens and transferred to the laying hens' house and kept in individual cages to measure egg-laying performance. Seven cages (length 37 cm × width 34 cm × height 34 cm) were stacked on one of three tiers in each bank; 15 banks were arranged in one row, for three rows in total. Some chickens from the same hatch were randomly selected and used for a different experiment. The average survival till 18 weeks was similar for the four genetic groups and ranged from 96.0 to 97.5%. Chickens were fed the same diet ad libitum and kept under the same controlled environmental conditions. The daily lighting increased by one hour per week from 8 h at 19 weeks of age to 13 h at 24 weeks of age, and increased by 0.5 h per week from 25 to 30 weeks of age. A constant daily lighting of 16 h was maintained thereafter.The average weight of the first three eggs of each bird was calculated and will be referred to as the first egg weight. Egg weight was measured every four weeks from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg weights were measured using a digital scale with a sensitivity of 0.01 g .Age at first egg and cumulative egg number till 43, 72, and 100 weeks of age  were computed from individual egg-laying recordings. The oviposition period, representing the average time interval between two successive layings was calculated following the method of Blake et al. . BrieflyEgg quality traits were measured within 24 h after egg collection at 32, 54, 72, 86, and 100 weeks of age and included egg shape index, eggshell colour, eggshell strength, eggshell thickness, eggshell ratio, yolk colour, yolk ratio, and Haugh unit. The egg shape index was calculated as the ratio of egg width to egg length [https://www.r-project.org/).For egg production traits, chickens that did not lay any eggs  were removed from the dataset. Subsequently, outliers were removed by excluding data that deviated more than three standard deviations from the mean of their genetic group  method in ASReml 4.2  using thedigree” . For eggFor egg weight and egg quality traits, which had multiple observations, the phenotypic data was analysed using Model (1) extended with a permanent environmental effect:Phenotypic and genetic correlations between traits, as well as within traits measured at different ages, were estimated using bivariate models. The fixed effects were the same as for the univariate models. The additive genetic effects were assumed to be distributed as:In the bivariate version of Model (2), permanent environmental effects for the two traits were assumed distributed as:r) were estimated based on the weighted averages.The above models assume genetic variance was the same for the four genetic groups. To evaluate possible differences in genetic variances between genetic groups, we also estimated heritabilities with the multivariate equivalent model of the previously described univariate model, with each trait modelled as three traits: one for WW, one for YY, and one for the crossbreds (WY and YW). In this multivariate model, residual and permanent environmental covariances between the three genetic groups were set to zero, because each individual only has phenotypes pertaining to one of the groups. Genetic covariances between the groups were also fixed to zero, to avoid that the estimate of genetic variance for a group was affected by information from the other genetic groups. Weighted averages of all variance components were estimated, using the proportion of animals in a genetic group as weights, i.e., 0.22 for WW, 0.25 for YY, and 0.53 for crossbreds. Heritability . Given the large number of traits evaluated, the p-values to declare significance were adjusted for multiple testing by computing false discovery rates (FDR) based on the Benjamini and Hochberg method [Estimated differences between the predicted mean of YW and the parental mean, between the predicted mean of WY and the parental mean, and between the predicted means of YW and WY, as well as the Wald F statistics for these contrasts, were obtained using the “!CONTRAST” qualifier in ASReml 4.2. Because average pause length was not normally distributed, which might affect med data . This trg method  using thDescriptive statistics for egg weight, egg production, and egg quality till 100 weeks of age are in Table Estimates of heritabilities, repeatabilities, and phenotypic variance for egg weight traits are in Table g = − 0.70) and EN72 (rg = 0.46), and moderately with average clutch length at 43 weeks of age (rg = 0.29) and oviposition period (rg = − 0.36). EN72 was estimated to be negatively genetically correlated with oviposition period (rg = − 0.40), and number of clutches at 72 weeks of age (rg = − 0.54). EN100 was positively correlated with EN72 (rg = 0.90), with average clutch length at 43 weeks of age (rg = 0.69), and with average clutch length at 72 weeks of age (rg = 0.57). Bivariate models that included average pause length at 43, 72 or 100 weeks of age did not converge.Estimates of heritabilities and phenotypic variance for egg production traits are in Table Estimates of heritabilities, repeatabilities, and phenotypic variance for egg quality traits at 32, 54, 72, 86, and 100 weeks of age are in Table FDR ≤ 0.01). Heterosis for egg weight increased with age. The egg weights of YW and WY were higher than the best parental breed, WW, from 56 and 76 weeks of age, respectively. Estimates of reciprocal cross differences were not significant for egg weights measured along the laying period, ranging from − 4.1 to − 1.5% (FDR > 0.05).Predicted averages and estimates of heterosis for egg weight are in Table FDR ≤ 0.05). The estimate of heterosis for oviposition period was not significant (FDR > 0.05). Average clutch length till 43, 72, and 100 weeks of age showed high and negative heterosis (FDR ≤ 0.01). Favourable and significant heterosis for average pause length was observed for WY at 72 weeks of age (FDR ≤ 0.05), and for both crossbreds at 100 weeks of age (FDR ≤ 0.01). The difference between the reciprocal crosses was not significant for any egg production trait (FDR > 0.05).Predicted averages and estimates of heterosis for egg production traits are in Table FDR ≤ 0.01) and for eggshell colour and Haugh unit for WY (FDR ≤ 0.01). Eggshell strength of YW showed greater heterosis than other egg quality traits at early laying stages, 10.3, 12.6, and 11.4% at 32, 54, and 72 weeks of age, respectively. The difference between reciprocal crosses was significant for eggshell strength at 54 weeks of age (FDR ≤ 0.05) but not for other ages or other traits.Predicted averages and heterosis for egg quality traits are in Table The objective of this study was to analyse the genetic background and estimate heterosis and the reciprocal cross differences for egg-laying performance for crosses between an indigenous chicken breed and a commercial layer breed throughout the laying period. It should be noted that the model that we used for analysis of crossbred performance is an alternative parameterization of the so-called Dickerson model that has been used previously in several other studies , 30. Thep-value = 0.16, as evaluated with the paired t-test). Although the differences in estimates of heritability between the models were significant , the estimates were generally similar . These additional results show that the assumption of equal genetic variance for all the genetic groups, as made in the univariate model, hardly affected estimates of heterosis for the traits evaluated here.In addition to genetic parameters, we also estimated heterosis and reciprocal cross differences with the multivariate model. The difference between the univariate model and the multivariate model ranged from − 1.04 to 1.24 in percentage points for heterosis of WY, from − 0.81 to 1.72 for heterosis of YW, and from − 1.58 to 1.52 for reciprocal cross differences  length. Heterosis for egg number at the beginning of egg laying could be partly explained by significant and favourable heterosis for age at first egg. Heterosis for egg number at the late laying stage can be explained by the high and favourable heterosis for average pause length.We reported heterosis for traits reflecting egg shape, eggshell, albumen, and yolk qualities. Eggs from both crossbreds showed substantial heterosis for eggshell colour, suggesting that the crossbreds have a lighter brown eggshell colour than the parental mean. This is favourable for some markets where eggs with a light brown colour are more popular than those with a dark brown colour, which is the case in China . For eggAfter adjusting for multiple testing, the reciprocal cross differences were significant only for eggshell strength at 54 weeks of age. However, we found that the degree of heterosis was quite different between reciprocal crosses. Egg weight heterosis ranged from 1.0 to 5.7% for WY and from 4.0 to 9.0% for YW; heterosis for eggshell strength at the early laying stage ranged from 0.9 to 4.8% for WY and from 10.3 to 12.6% for YW. These findings are similar to previously reported results , 19. SexHeterosis is expected to be the result from non-additive effects . Narrow Heterosis for egg weight and cumulative egg number is substantial and increased with age in crossbreds of Beijing-You and White Leghorn chickens. Crossbreds have better egg production persistency than the superior White Leghorn parent. The degree of heterosis differed significantly between the reciprocal crosses. These findings can contribute to establishing effective poultry breeding schemes that use indigenous and elite lines for niche markets.Additional file 1: Table S1: Percentage of outliers for egg weight traits. The data were filtered by the mean ± three standard deviations to remove the outliers. Table S2. Percentage of outliers for egg production traits. The data were filtered by the mean ± three standard deviations to remove the outliers. Table S3. Percentage of outliers for egg quality traits. The data were filtered by the mean ± three standard deviations to remove the outliers.Additional file 2: Figure S1. Genetic and phenotypic correlations between the same egg quality trait at different ages.Additional file 3: Tables S4. Heterosis and reciprocal cross differences of egg weight traits for the Dickerson model and comparison between the univariate model and the Dickerson model. Tables S5. Heterosis and reciprocal cross differences of egg production traits for the Dickerson model and comparison between the univariate model and the Dickerson model. Tables S6. Heterosis and reciprocal cross differences of egg quality traits for the Dickerson model and comparison between the univariate model and the Dickerson model.Additional file 4: Table S7. Heterosis of egg weight traits for multivariate model and comparison between the univariate model and the multivariate model. Table S8. Heterosis of egg production traits for multivariate model and comparison between the univariate model and the multivariate model. Table S9. Heterosis of egg quality traits for multivariate model and comparison between the univariate model and the multivariate model.Additional file 5: Figure S2. Spearman correlation coefficient between the univariate and the multivariate model for heritabilities (a) and repeatabilities (b).Additional file 6: Table S10. Variances, heritabilities, and repeatabilities of egg weight traits for Beijing-You, White Leghorns, and crossbreds. Table S11. Title: Variances, heritabilities, and repeatabilities of egg-production traits for Beijing-You, White Leghorns, and crossbreds. Table S12. Variances, heritabilities, and repeatabilities of egg quality traits for Beijing-You, White Leghorns, and crossbreds.Additional file 7: Table S13. Variances for egg weight traits at different ages with the univariate model. Table S14. Variances for egg quality traits at different ages with the univariate model.Additional file 8: Figure S3. Heterosis of egg number at separate periods for reciprocal crosses. Figure S4. Heterosis of cumulative egg number for reciprocal crosses.Additional file 9: Table S15. Predicted albumen height for the four genetic groups and heterosis for reciprocal crosses.Additional file 10:Figure S5. Spearman correlation coefficients between average heterosis and heritability for each trait.


DeepPheWAS is an R package for phenome-wide association studies that creates clinically curated composite phenotypes and integrates quantitative phenotypes from primary care data, longitudinal trajectories of quantitative measures, disease progression and drug response phenotypes. Tools are provided for efficient analysis of association with any genetic input, under any genetic model, with optional sex-stratified analysis, and for developing novel phenotypes.https://github.com/Richard-Packer/DeepPheWAS.The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at Bioinformatics online. Phenome-wide association studies (PheWASs) can be used to better understand the pleiotropic effects of genetic variants  and retrieve results only for genetic models tested. The user cannot specify when to use new data fields or updates to existing data fields , and the user cannot specify their preferred statistical approach and outputs, such as false discovery rate (FDR).The platform we have developed, DeepPheWAS, addresses both phenotype generation and efficient association testing while incorporating the following developments that are not yet available in any single current platform or online resource:Clinically curated composite phenotypes for selected health conditions that integrate different data types (including primary and secondary care data) to study phenotypes not well captured by current classification trees;Integration of quantitative phenotypes from primary care data, such as pathology records and clinical measures;Integration of disease progression phenotypes, longitudinal trajectories of quantitative measures and drug response measures;Clinically curated phenotype selection for traits that are extremely highly correlated;Efficient association testing, and type-1 error control using PLINK 2 firth fall-back regression.Flexible tests of additive, dominant, recessive and genotypic models;Inclusion of complex variants, such as copy number variants with a wide range of copy numbers ;Ability to test genetic risk scores;Creation of phenotypes in sex-specific strata to run a sex-stratified PheWAS;Providing tools for generating novel phenotypes using a simple phenotype mapping process.NFAT5 and TERF2), previously associated with urate levels , shown to be associated with carotid intima-media thickness (IMT) (We applied our DeepPheWAS approach to rs2912062 (nearest genes ss (IMT) .SERPINA1 Z allele [rs28929474(T)] under additive and recessive genetic models. Patterns of association differed between models, including opposite estimated effect directions for this variant on forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), consistent with previous findings (To demonstrate the flexibility of DeepPheWAS we assessed the findings .CCL3L1 ranges from 0 to 8 in UK Biobank participants (CCL3L1 copy number (coded 0–8) under a linear additive model; no associations reached an FDR threshold of 1%  aggregate multiple SNPs, providing improved power for studying phenotypic associations, but cannot be specified in online PheWAS platforms. We performed a PheWAS using a 279-variant GRS for FEV1/FVC , which s alleles . FurtherCASP7 and NRAP) and rs7697189 (nearest gene HHIP). We replicate association for risk of cataract in women only (1 in men compared to women (We applied DeepPheWAS to rs12777332 (nearest genes men only  for rs12to women  for rs76https://richard-packer.github.io/DeepPheWAS_site/.DeepPheWAS is an R package that can be run on high-performance computing clusters and requires R 4.1.0 and PLINK 2.0. DeepPheWAS is optimized for UK Biobank data and is expected to be interoperable with the UK Biobank Research Analysis Platform, further details on required data can be seen in the supplement and on https://github.com/Richard-Packer/DeepPheWAS.The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at Here, we present DeepPheWAS, an R package that facilitates phenome-wide association studies while addressing several limitations of existing approaches. This includes the ability to analyse a broader range of phenotypes derived from large-scale electronic healthcare records, more informative composite phenotypes, greater flexibility in the type of genetic variation that can be studied and assessing associations with genetic risk scores.btad073_Supplementary_DataClick here for additional data file.


Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general.No phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast The online version contains supplementary material available at 10.1186/s12862-023-02164-4. Many phenotypic traits covary during evolution. For example, the logarithm of brain weight and that of body weight show a nearly perfect linear relationship across mammals , 2. In tDespite a long-standing interest in trait correlation in evolution , 13, 21,Related to trait-trait correlation is the concept of modularity. It has been hypothesized that it is beneficial for organisms to have a modular organization such that functionally related traits belonging to the same module covary and genotypes and/or phenotypes that lead to low fitness are less likely to occur , 45–47. Saccharomyces cerevisiae that have been measured in 4817 single-gene deletion lines [Drosophila melanogaster [To gain a general mechanistic understanding of trait-trait coevolution, we study the phenotypic correlations for a large number of trait pairs at the levels of mutation and long-term evolution; natural selection is inferred when the evolutionary correlation between traits cannot be fully explained by the mutational correlation. We also ask if the overall pattern of trait correlation  differ at the two levels. Our primary data include 220 cell morphology traits of the budding yeast on lines , 89 mutaon lines , and 16 on lines , 51. Theon lines . Subsequon lines , but theon lines . To verinogaster . At lastCORM, defined as Pearson’s correlation coefficient across 4,817 gene deletion lines  have a CORE that deviates significantly from CORM at the false discovery rate (FDR) of 5%  from all pairwise comparisons. Hence, there is no indication that using overlapping trait pairs has biased the estimate of the fraction of trait pairs with significantly different CORE and CORM.We found that the frequency distribution of ORM Fig. B, sugges5% Table , Data S1M used as the mutational input, generating 1,000 simulated datasets. Before the simulation, we confirmed that the sampling error of our estimated M is negligible, likely because of the large number of mutants used in M estimation   lines [CORE from the corresponding CORM from gene deletion lines and that using CORM from MA lines, we found 990 trait pairs to exhibit selection signals and fall into the same category in both analyses, including 275 pairs in the “strengthened” category, 223 pairs in the “weakened” category, and 574 pairs in the “reversed” category. All of these numbers substantially exceed the corresponding expected random overlaps , suggesting the reliability of both analyses. Although mutations in MA lines are more natural than those in gene deletion lines, the number of MA lines is much smaller than the number of gene deletion lines and only 187 of the original 220 traits were measured in the MA lines. For these reasons, we focused on the CORM estimated from the gene deletion lines in subsequent analyses.To assess the robustness of the selection signals detected, we repeated the above analysis using A) lines   Fig. A. Unlession Fig. B. Finallion Fig. C.Fig. 3SCORE, between the traits across the replicate lineages at the end of the simulated evolution. This was repeated 200 times to obtain an empirical distribution of CORE. To evaluate the difference between CORM and CORE, we examined the location of CORM in the distribution of CORE; a significant (P < 0.05) difference is inferred if CORM is in the left or right 2.5% tail of the CORE distribution.To verify these predictions, we simulated the evolution of two traits. Under each parameter set, we simulated 50 independent replicate lineages and computed the correlation coefficient, or CORE is centered around CORM  of the correlation matrix from divergent lineages and mutants, respectively. A greater Veigen corresponds to a stronger overall correlation between traits because the eigenvalues become less evenly distributed as the absolute values of the correlation coefficients become larger [Veigen; a matrix estimated from a smaller sample naturally tends to have fewer positive eigenvalues and greater Veigen. To exclude the influence of this factor, we randomly sampled the mutant strains to obtain 5000 control datasets. Because the rank number of the evolutionary correlation matrix is 15 for the yeast data , each control dataset also consists of 15 randomly drawn strains such that the corresponding mutational correlation matrix also has 15 positive eigenvalues. We examined the location of the observed Veigen in this distribution and computed a P-value based on this location . For the yeast traits, Veigen of the observed evolutionary correlation matrix exceeds that in 96% of control datasets . Hence, there is little evidence for a difference between the overall evolutionary correlation and the overall mutational correlation in yeast. For the fly data, the number of positive eigenvalues is unlimited by the sample size for both the evolutionary and mutational correlation matrices, hence we directly compared Veigen between the two matrices, but found them to be similar , where n is the number of traits examined. Veigen/(n-1) equals 0.157 and 0.268 for the yeast mutational and evolutionary matrices, respectively, whereas the corresponding values in flies are 0.153 and 0.190, respectively.While all of the above analyses focused on individual trait pairs, here we ask whether the overall trait correlation across divergent lineages is stronger or weaker than that created by mutation. As a measure of the overall level of trait correlation , we calculated the variance of eigenvalues (e larger . HoweverCR) test [CR for the evolutionary covariance matrix and compared it to the CR distribution based on 5000 mutational covariance matrices estimated from the randomly drawn subsets of mutants aforementioned. We treated the three non-overlapping categories of the yeast morphological traits—actin traits, nucleus traits, and cell wall traits [CR of the evolutionary covariance matrix exceeded that of every control dataset  test  that coml traits —as threeCORM) with that across divergent lineages (CORE) for 24,090 pairs of yeast cell morphology traits and 276 pairs of fly wing morphology traits, we detected the action of natural selection in trait-trait coevolution. The fraction of trait pairs showing evidence for selection is substantially higher in the fly (52%) than yeast (28%) data . This is at least in part caused by a difference in statistical power, because the number of strains/species used for estimating CORE is much greater for the fly (110) than yeast (16) data. It is likely that a higher fraction than 28% of the yeast trait pairs are subject to selection in their coevolution. Furthermore, our comparison between CORE and CORM intends to test selection on trait correlations common among the evolutionary lineages considered. If different evolutionary lineages have different trait correlations, the CORE estimated from all lineages may not be significantly different from CORM even when selection occurs in some or all of the lineages. In other words, our test is expected to underestimate the proportion of trait pairs subject to selection.By comparing the trait-trait correlation across mutants ; consequently, We demonstrated by simulations that various selection regimes can explain differences between aptation , 43, 59.d traits .CORE and CORM reported here contains a component caused by the environmental difference in phenotyping. Notwithstanding, our analysis suggests that this component is small , which is expected because both media are meant to provide an ideal, stress-free environment for yeast growth. This said, future phenotyping in the same medium will be needed to validate our findings.It is worth noting that the yeast natural strains had been cultured in synthetic media before phenotyping  while thCORE and CORM. These trait pairs may be divided into two groups. In the first group, CORE and CORM are actually different, but the difference is not found significant due to the limited statistical power. As mentioned, we believe that a substantial fraction of yeast trait pairs belong to this category due to the relatively low statistical power in detecting the difference between CORE and CORM in the yeast data. In the second group, CORE truly equals CORM, which could result from one of the following three scenarios. First, the specific trait-trait correlation does not impact fitness so evolves neutrally. Second, the two traits have an intrinsic, immutable relationship , so will yield equal CORE and CORM; this possibility can be tested by examining the correlation of the two traits across isogenic individuals that show non-heritable phenotypic variations [CORM via a second-order selection [While selection was detected for many trait pairs, a large fraction of trait pairs, especially in the yeast data, do not show a significant difference between riations . The laselection , 62, 63 In addition to pairwise trait correlations, we tested hypotheses regarding the evolution of overall phenotypic integration and modularity. In the yeast data, we observed a higher modularity across natural strains than across mutants but did not find evidence for a change of overall phenotypic integration in evolution. These results support the view of increasing modularity during evolution , 46, 64 CORM estimated from one yeast strain (BY) with CORE estimated from 16 different strains, under the assumption of a constant CORM across different strains. While it is a common practice to assume that the mutational architecture is more or less constant during evolution and to study phenotypic evolution by comparing mutational or genetic (co)variances in one species with those among different species [CORM. For instance, it has been hypothesized that the optimization of mutational (co)variances driven by selection for mutational robustness and/or adaptability can lead to modularity [Drosophila gene expression traits that variational modules identified from mutants can be predicted to some extent by functional grouping of genes , although there is still much difference between functional modules and modules resulting from mutational pleiotropy, suggesting that optimization of the mutational architecture is far from complete even if it did take place [CORM could still vary across strains because the pleiotropic effects of a mutation can vary by the environment and genetic background [CORM evolves, how rapidly it evolves, and whether its evolution is largely neutral or adaptive.Our analysis compared  species , 65, 66, species –69. As ddularity , 46. It ke place . Even wickground , 70, 71.CORE estimate may not be very accurate, and the selection test suffers from low statistical power. It would be desirable if more non-mosaic strains from non-Wine/European clades are included. Another caveat regarding the calculation of CORE is that correction methods like independent contrast do not always sufficiently account for the tree structure and can be susceptible to singular evolutionary events  [Our analysis of the yeast dataset is subject to a major limitation resulting from the structure of the dataset. As many yeast strains are mosaic, only a small number of strains 16) were used in our study. Most of the remaining strains fall in one clade  ; in our In summary, we detected the action of natural selection in shaping trait-trait coevolution. Because the traits analyzed here, especially the yeast traits, were chosen almost exclusively due to their measurability, our results likely reflect a general picture of trait-trait coevolution. Measuring these yeast traits in additional divergent natural strains with clear phylogenetic positions could improve the statistical power and clarify whether the fraction of trait pairs whose coevolution is shaped by selection is much greater than detected here. Finally, the detection of selection for enhanced modularity of the yeast traits analyzed supports the hypothesis that modularity is beneficial , 25. TheCan trait-trait correlations in long-term evolution be explained by mutations? Our analyses showed that some correlations observed across divergent lineages differ significantly from correlations created by mutations. In addition, the pattern of phenotypic covariance among natural yeast strains has stronger modularity  than among mutants. These observations together indicate that selection likely played a role in shaping trait correlations in long-term evolution.What evolutionary forces drive trait-trait correlation during evolution? Our simulations show how various selection regimes render the pattern of correlation during evolution different from that caused by mutation. Some types of differences, including strengthening and reversal of correlations, are explained by selection for an optimal allometric relationship, but not selection on individual traits.In this study, we analyzed morphological traits of yeast and flies and compared patterns of trait-trait correlation at the levels of mutation and long-term evolution. In both datasets, we discover that the evolutionary correlation differs significantly from the mutational correlation for numerous trait pairs, revealing a role of natural selection in trait-trait coevolution. We also provide evidence for selection for enhanced modularity of the yeast traits. Insights gained in this study can be summarized as follows:S. cerevisiae cell morphology traits were previously measured by analyzing fluorescent microscopic images. Three phenotypic datasets were compiled and analyzed in this study, including (i) 220 traits measured in 4,718 gene deletion lines that each lack an nonessential gene [The ial gene , (ii) thial gene , and , cell wall traits , and nucleus traits  , 51. TheZ-test. The standardized value of the ith trait in the jth strain is Before the analyses, we first standardized all trait values by converting each trait value to the natural log of the ratio of the original trait value to a reference such that the distributions become approximately normal and suitable for the Drosophila melanogaster and a mutational covariance matrix was estimated [The locations of 12 vein intersections on the fly wing were previously measured in 150 MA lines of stimated . Becausestimated . Both maM matrix) of yeast or fly, we took samples (vectors of phenotypes) from the multivariate distribution of M , estimated a covariance matrix  test. For each pair of predefined modules, traits were first re-ordered such that traits belonging to each module were located in the upper-left and lower-right corners of the covariance matrix, respectively, and M [CR values was used to represent the overall modularity. A test for selection on CR was performed following the test of selection on Veigen.To test whether there exists a significant modular structure among traits, we performed the covariance ratio , D is the Euclidean distance defined by d1 and d2 are the distances between the current phenotypic values of the two traits and their corresponding optima, respectively. When there is a fitness ridge , D is the shortest distance from the current phenotype to the fitness ridge. The selection coefficient s equals f and WT are the fitness values of the mutant and wild type, respectively. The fixation probability of a newly arisen mutant is Ne was set at 104. After each unit time, the phenotypic effect of each mutation is added to the population mean with a probability of We considered a Gaussian fitness function of pulation , where tentclass1pt{minimaP = 0.54, t-test).In simulations where different lineages were assigned different optima, each lineage’s optimum was obtained by independently drawing the optimal values of the two traits from the standard normal distribution. Before conducting simulations, we confirmed that the optima of the two traits are not correlated , where m1 and m2 are the effects on trait 1 and trait 2, respectively. The rescaled mutational effect, To investigate the effect of mutational bias on trait correlation, we introduced the bias coefficient CORM estimated from the rescaled effects  sites. Scale bar indicates genomic divergence level. The tree was based on the distance matrix downloaded from http://1002genomes.u-strasbg.fr/files/1011DistanceMatrixBasedOnSNPs.tab.gz. The inset at the top left coner shows the tree topology but the branch lengths are not drawn to scale. Fig. S2. Mutational (CORM) and evolutionary (CORE) correlations for all pairs of the 187 yeast morphological traits. CORM is based on yeast mutation accumulation lines. (A) CORM (upper triangle) and CORE (lower triangle) for all pairs of traits ordered according to their IDs. (B) Frequency distributions of CORM (blue) and CORE (red) across all trait pairs. The two distributions are significantly different . Fig. S3. Mutational (CORM) and evolutionary (CORE) correlations for all pairs of the 24 fly wing morphological traits. (A) CORM (upper triangle) and CORE (lower triangle) for all pairs of traits ordered in the same way as in the original dataset. (B) Frequency distributions of CORM (blue) and CORE (red) across all trait pairs. The two distributions are significantly different .