It is crucial to understand how targeted traits in a hybrid breeding program are influenced by gene activity and combining ability. During the three growing seasons of 2015, 2016, and 2017, a field study was conducted with twelve cotton genotypes, comprised of four testers and eight lines. Thirty-two F1 crosses were produced in the 2015 breeding season using the line x tester mating design. The twelve genotypes and their thirty-two F1 crosses were then evaluated in 2016 and 2017. The results demonstrated highly significant differences among cotton genotypes for all the studied traits, showing a wide range of genetic diversity in the parent genotypes. Additionally, the line-x-tester interaction was highly significant for all traits, suggesting the impact of both additive and non-additive variations in gene expression. Furthermore, the thirty-two cotton crosses showed high seed cotton output, lint cotton yield, and fiber quality, such as fiber length values exceeding 31 mm and a fiber strength above 10 g/tex. Accordingly, selecting lines and testers with high GCA effects and crosses with high SCA effects would be an effective approach to improve the desired traits in cotton and develop new varieties with excellent yield and fiber quality. Cotton is the world's most significant fiber commodity and Egypt's most important cash crop. In 2022, the global total area harvested, yield, and production of cotton were 32.65 million hectares, 811 kg per hectare, and 121.57 million 480-pound bales, respectively. In Egypt, the overall harvested area, yield, and production of cotton were 0.1 million hectares, 718 kg per hectare, and 0.33 million 480-pound bales. Cotton production fell 8.83% globally and 53.49% in Egypt in the 2021/22 cropping season compared to the previous year . The major aim of cotton breeders is to look for and select genotypes with high-yield traits, as well as utilizing the genetic diversity that Egyptian cultivars still have untapped. Significant effort has been made in Egypt to improve cotton's productivity and quality attributes [To create a breeding program for the economic exploitation of heterosis for hybrid crop development or variety creation, Parents need to be genetically superior, physiologically effective, and have improved general and particular combining abilities . Heteros2D estimates were higher than those of σ2A ones for these traits. Additionally, they demonstrated that the cotton variety "Giza 90" was a good general combiner for the first fruiting node [. [Determining genetic variation and combining ability are helpful breeding tools to assess the breeding potential of some populations or parents, assisting cotton breeders in using the right breeding techniques , 12. Theing node . Xiaoqua node [.  studied  node [.  demonstr node [. .The main objectives of this study were: 1) to use line × tester analysis to assess the combining abilities, GCA and SCA, among different cotton genotypes; 2) to identify genetic variation components for earliness, yield characteristics, and fiber quality variables, as well as heritability; 3) to ascertain if hybrid breeding may take advantage of genotype variation to improve yield and quality parameters depending on their best GCA and SCA characteristics; and 3) to investigate the nature of gene activity. Moreover, improving cotton production and fiber properties by developing cotton crosses with varied genetic origins will offer helpful and reliable knowledge for cotton breeding programs.Twelve cotton genotypes from six distinct origins were the genetic resources utilized in the current study. These twelve cotton genotypes' origin, pedigree, and characteristics are shown in Table The selfed seeds of twelve cotton genotypes were crossed using a line x tester mating design with four testers and eight lines to generate thirty-two cotton hybrids. According to Kempthorne , the eigi.e., first fruiting nods (FFN), number of fruiting branches per plant (No. FB/P), plant height (PH), seed cotton yield tone/hectar (SCY.t/ha), lint cotton yield tone/ hectar (LCY.t /ha), seed index (SI) in gram, number of open bolls per fruiting branches (No.B/FB), lint index (LI) and number of seeds/boll (No. S/B).Data were recorded on ten guarded plants for earliness, growth habit, cotton yield, and fiber traits Fiber parameters were recorded in the laboratories of the Cotton Technology Research Division, Cotton Research Institute, Agriculture Research Center, Egypt, in accordance with the standard method of the American Society for Testing Materials Designation  at 21 °C ± 1 °C and 65% ± 2% relative humidity. Micronaire reading (MIC) was used to express fiber fineness. Fiber strength (FS) was measured by using a Pressley instrument at zero gauge (g/tex). Fiber strength is closely related to yarn and fabric strength and spinning efficiency. Fiber length at 2.5%  was estimated as the space in mm spanned by 2.5% of the fibers as recorded on a digital fibro graph. The uniformity ratio (UR%) was calculated in accordance with the following equation:The recorded data were analyzed using Steel and Torrie's method of variance analysis  to deterTwelve studied traits were used to perform the cluster analysis using Euclidean distance via the unweighted paired group method with arithmetic average (UPGMA). This cluster was basically performed depending on Jaccard similarity coefficient of 12 cotton genotypes based on yield and yield components traits across two years of study as shown in Table The analysis of variance for each year between all the studied traits are presented in Table General performance among all parental genotypes and F1 crosses is shown in Table The phenotypic mean performances of the 32 top crosses were differing from one season to the other season for all the studied traits as shown in Tables Estimates of general combining ability effects of lines and testers for all the studied traits in each year are presented in Table For fiber quality measures, the parental line G.89 × G.86 had a GCA effect of 0.16 and 0.18 for FS., respectively, while G.94  for UHM. While TNB and G. 94 showed a preferable GCA effect for FS, in both seasons respectively. Data showed that the best desirable general combining ability effects for FFN/P, number of fruiting branch/plant, and plant height was TNB which had highly significant and positive GCA. While CB 58 and Pima s6 had highly significant and positive GCA for FFN and number of fruiting branch/plant. On the other hand, undesirable general combining ability effects showed highly significant and negative GCA Giza 89 × Giza 86 and Giza 94 for FFN and Suvin and Giza 94 for plant height.The testers Giza 93 and BBB exhibited highly significant positive (desirable) general combining ability effects FFN and plant height, respectively. While the highly non-significant negative (undesirable) general combining ability affects Giza 86 and Karshenky for FFN/P; Giza 93 for fruiting branch/plant and plant height for Karshenky. For fiber quality traits the lines showed those genotypes; Giza 89 × Giza 86, Pima s6, and CB 58 had highly significant and positive GCA for micronaire read (Table 70 had highly significant positive SCA (desirable) effects for FFN and another two hybrids (Giza 86 × Giza 70 and Karshenky x Giza 94) exhibit highly significant positive SCA (desirable) effects for number of fruiting branch/plant. Also, five hybrids Giza 86 × CB 58, Karshenky x Giza 94, Giza 93 x (Giza 89 × Giza 86), BBB x TNB, and BBB x Giza 70 exhibited highly significant positive SCA (desirable) effects for plant height in each year and over the two years.Estimates of specific combining ability effects of the 32 Egyptian cotton hybrids for all the studied traits for each year and the combined over two years are presented in Tables The three studied fiber quality; two cotton hybrids (Giza 86 × Giza70 and BBB x Pima S6) showed highly significant negative SCA (desirable) effects for micronaire reading, while another four hybrids Karshenky x Giza 94, G.93 × Pima S6, BBB x (Giza 89 × Giza 86) and BBB x Giza70 had highly significant positive SCA (undesirable) effects. Two hybrids Giza 86 × Giza 96 and Giza 93 × Suvin had highly significant and negative SCA (undesirable) effects and only one cross Giza 93 × Giza 96 exhibited highly significant and positive SCA (desirable) for fiber strength during each year and over the two growing seasons. Four cotton hybrids Giza 86 × Pima S6, Karshenky x Giza 96, Karshenky x Pima S6, and Giza 93 × Suvin exhibit highly significant and positive SCA (desirable) for fiber length over the two years only. While during each year the four hybrids showed non-significant SCA. Thus, the significance of GCA (variances due to lines and testers) and SCA (variances due to lines x testers) implied that both additive and non-additive types of variation were available for all the characters, yet additive genes were more important than the dominant genes because variance due to GCA was higher than that of SCA.Variability in genetic distance and origin was a crucial factor in creating new recombination that led to the phenomenon of heterosis. Heterosis is defined as the deviation in the performance of an F1 hybrid compared to its mid-parent or better-parent and results in improved adaptation due to the superiority of the F1 hybrid over its parents in one or more characteristics. The mid-parent heterosis was followed in the current study and showed that all thirty-two hybrids studied exhibited both positive and negative heterosis for all the twelve measured traits Table . For SCY) showed that significant positive correlations were exhibited amongst trait pairs, the correlation between No. FB/P and SCY.K/Fad (0.81), SCY. t/ha. and LCY. t/ha. (0.98), No.FB/P and LCY. t/ha (0.79), LCY. t/ha. and L.I. (0.71), S.C.Y. t/ha and LI (0.61) were greatest, while the least correlation was observed between SCY.t/ha, LCY. t/ha and No. FB/P (0.33). Also, correlations among the fiber traits were also significant positive, except for the correlation of FF with each of FS (-0.32), UR% (-0.46), and UHM (-0.49) whereas other pairs of traits showed non-significant correlations, including No. S/B with all traits, FS with PH, SCY. t/ha, LCY. t/ha, LI, No. FB./P, and No. B/FB  had the highest indirect contribution to seed cotton yield, followed by NO. B/FB (r = 0.40), and LI (r = 0.30) indicating the importance of these traits to LCY. t/ha. This needs to be carefully considered simultaneously when selecting for yield improvement in cotton.The result of direct and indirect correlation coefficients regressed with seed cotton yield was presented in Fig. The potential of genetic material to transfer the desired features to the offspring plays a key role in the breeding program's ability to enhance crops , 29. CriThe four testers were clearly divided into two distinct clusters, which may be due to the variation among those male parents in GCA for most studied traits. Also, the two imported female parent genotypes Giza 70 and Giza 93 were clustered together, showing close relationship and similar performance. On the other side, the parental genotypes Giza 96, Giza 94, BBB, and Giza 89 X Giza 89 were grouped together with narrow genetic base which could be attributed to their performance as poor general combiners for most studied yield and fiber quality traits. Several investigations exploited the phenotypic performance to classify cotton genotypes into different clusters as genotypes from the distant clusters could be favorably utilized in crossing program to obtain a transgressive segregant in early generations , 32–37.The highly significant differences between studied cotton genotypes revealed by ANOVA in the current study for both agronomic and fiber quality traits indicated the existence of genetic diversity among those genotypes. These findings demonstrated the magnitude of utilizing the line x testers method in such cases. For all examined traits, the variance attributable to parents vs. crosses was also statistically significant, indicating the role of hybrid vigor existing for these traits, whereas the line x tester was highly significant, demonstrating the significance of both additive and nonadditive variance. Similar findings were reported in previous investigation on barbadense cotton , 38–40. P < 0.01) for all characteristics, demonstrating the existence of considerable genetic variation among all genotypes; thus, additional subsequent research was carried out to evaluate the combining ability [Twelve yield, yield component, and fiber quality parameters were assessed on the resultant crosses and the twelve parental genotypes. For heterosis breeding to be used effectively, the genetic variance between female parental genotypes and tester lines must be evaluated. Our findings showed that genetic variations among the 44 genotypes were extremely significant  gene activity , 95. GCAThe cotton hybrids discovered in this work can be used as a source of high-yielding germplasm. By using marker-assisted parental selection to find the marker associated with the genes that the best values of targeted traits may be introduced. Genome-assisted backcrossing can then be used to effectively transfer those genes into other germplasms. Utilizing genome editing methods like CRISPR-Cas9, which don't produce GMOs and are generally recognized as non-transgenic, it is possible to transfer the gene . Modern The variances of all source variation determined by line-x tester analysis revealed that the majority of evaluated features differed significantly. The interaction lines × testers made a significant contribution to the expression variances for most studied traits. The lines Suvin, and Pima S6 as well as the tester BBB remarkably increased cotton yield and most investigated traits compared to the other lines and testers. Finally, the three hybrids above are considered potential promising genotypes to be best exploited and utilized in future cotton breeding efforts to generate hybrid cotton and increase yield, its constituent parts, and fiber quality attributes.


Background: While iron is essential for normal brain functioning, elevated concentrations are commonly found in neurodegenerative diseases and are associated with impaired cognition and neurological deficits. Currently, only little is known about genetic and environmental factors that influence brain iron concentrations.Methods: Heritability and bivariate heritability of regional brain iron concentrations, assessed by R2* relaxometry at 3 Tesla MRI, were estimated with variance components models in 130 middle-aged to elderly participants of the Austrian Stroke Prevention Family Study.Results: Heritability of R2* iron ranged from 0.46 to 0.82 in basal ganglia and from 0.65 to 0.76 in cortical lobes. Age and BMI explained up to 12% and 9% of the variance of R2* iron, while APOE ε4 carrier status, hypertension, diabetes, hypercholesterolemia, sex and smoking explained 5% or less. The genetic correlation of R2* iron among basal ganglionic nuclei and among cortical lobes ranged from 0.78 to 0.87 and from 0.65 to 0.97, respectively. R2* rates in basal ganglia and cortex were not genetically correlated.Conclusions: Regional brain iron concentrations are mainly driven by genetic factors while environmental factors contribute to a certain extent. Brain iron levels in the basal ganglia and cortex are controlled by distinct sets of genes. Iron is vital for normal brain function but iron excess can cause cell damaging oxidative stress , 2. BraiCurrently, only little is known about genetic and environmental factors that influence brain iron concentrations. Known genetic factors include iron metabolism genes, such as the hemochromatosis gene (HFE) and the transferrin gene (TF) –17. A ge2) which represents the phenotypic variance attributable to genetic effects. We quantified brain iron by magnetic resonance imaging (MRI) based R2* relaxometry, which was shown to strongly correlate with the mass spectrometry measured absolute iron content in post-mortem studies [In the current study we investigated the amount of genetic and non-genetic contribution to brain iron by determining the heritability : 61-75], ranging from 38 to 85 years. The study includes 71 (54.6%) females and 22 (16.9%) participants carry at least one APOE ε4 allele. Mean and standard deviation of the BMI are 25.6 +- 3.9 and 105 (80.8%), 13 (10%) and 110 (84.6%) individuals suffer from hypertension, diabetes and hypercholesterolemia respectively. Current or former smokers make up 45.4% of the study population .2=0.75, p=0.001) is similar to the cortex . The heritability is highest in the putamen  and the caudate  followed by the pallidum . In the cortex, we found the highest heritability in the temporal lobe (h2=0.76 p=0.001) and the lowest heritability in the frontal (h2=0.65 p=0.003) and the parietal lobe (h2=0.65 p=0.002). The variance in R2* iron due to age ranged from 1% to 12%, while sex explained only 2% of the variance of R2* iron in the frontal lobe.Univariate heritability estimates of R2* iron in the basal ganglia and in the cortex are presented in APOE ε4 carrier status explained up to 5% of the phenotypic variance in the basal ganglia . The effg) between 0.78 and 0.87, and between lobes, with rg between 0.68 and 0.97. The genetic correlation between frontal and occipital lobe was borderline significant with rg=0.65 and p=0.08. We did not find significant genetic correlation between basal ganglia and cortical lobe R2* rates, and neither environmental correlations between any of the investigated brain regions.The correlation of R2* rates between the different brain regions is summarized in Here we show that the heritability of R2* iron in basal ganglia and cortex in a general population of middle-aged to elderly individuals was moderate to high, with estimates ranging from 0.46 to 0.82. While age and BMI explained up to 12% and 9% of the variance in brain iron respectively, APOE ε4 carrier status, hypertension, diabetes, hypercholesterolemia and sex explained 5% or less, and smoking status did not explain any variance. We did not observe significant genetic correlations between R2* iron concentrations in basal ganglia and cortical lobes, but strong positive genetic correlation among cortical lobes and among basal ganglionic nuclei.2caudate=0.74, h2pallidum=0.46, h2putamen=0.82) are higher than those from the UK Biobank [2left caudate=0.38, h2right caudate=0.37; h2left pallidum=0.50, h2right pallidum=0.46; h2left putamen=0.50, h2right putamen=0.58), except for the pallidum. This discrepancy might be explained by the different heritability estimation methods: pedigree-based heritability in ASPS-Fam compared to heritability based on common single nucleotide polymorphisms (SNPs) in the UK Biobank [In all investigated regions, except for the pallidum, two-thirds or more of the variance in R2* iron can be explained by genetic factors. Our heritability estimates for R2* iron in the basal ganglia , when APOE ε4 begins to influence risk. The lack of association in our cohort might therefore be related to the low number of APOE ε4 carriers who were older than 65 years. However, in line with our study findings, a lack of association between APOE ε4 brain iron levels was also reported in the Memory and Aging Project, which is a clinical-neuropathological cohort study of older adults .The moderate to high phenotypic correlation of R2* brain concentrations among basal ganglia and among cortical lobes can be mainly attributed to genetic correlation. This reflects that the variance of brain iron in basal ganglia and in cortex is influenced to a large extent by the same genes, but due to the missing genetic correlation between basal ganglia and cortex, these sets of genes appear to be distinct from one another.Heritability estimates between 0.46 and 0.82 in our study imply that between 54% and 18% of the variance in brain iron can be explained by environmental factors. In our study, BMI had an effect on the variance of brain iron which confirms previous findings in a partly overlapping cohort . This reA limitation of this study is the rather small sample size which may prevent findings with smaller effect sizes. Moreover, heritability provides an estimate on the amount of variance in R2* brain iron that is determined by genetics but it does not allow the detection of specific genetic loci. Such loci could be identified by genome wide association studies, which unbiasedly screen the whole genome for genetic variants but also require very large sample sizes. A lack of genetic data for most of the study participants prevented us from investigating other genetic factors than APOE, such as iron metabolism-related genes like HFE and TF. Another limitation is the restricted battery of environmental factors examined. For instance, no information about diet or iron intake is available in this cohort but brain iron levels seem to be influenced by diet , 47. FurOur results demonstrate that brain iron accumulation is a complex and multi-factorial trait that is influenced by both, genetic and environmental, determinants. Although several genes that modulate brain iron levels are already known, further genetic studies may reveal additional genes, especially as it seems that brain iron concentration in the basal ganglia and cortex is influenced by distinct sets of genes. Concurrently, it would be interesting to examine potential environmental risk factors more closely in future studies.The ASPS-Fam is a prospective single-center community-based study on the cerebral effects of vascular risk factors in residents of the city of Graz, Austria, without clinical signs and symptoms of stroke and dementia and a normal neurological examination , 25. The3 resolution, 64 slices, 35 ms repetition time, 15° flip angle and 6 equally spaced echoes from 4.92 ms echo time with 4.92 ms echo spacing. R2* maps were processed by an in house developed optimized fitting algorithm [https://github.com/neuroimaging-mug/relaxometry). A semiquantitative T2-map was assessed by a T2-weighted sequence with 2 echoes . For assessment of regional R2*, the FreeSurfer toolset  [http://fsl.fmrib.ox.ac.uk/fsl) and subsequently eroded to prevent partial volume effects. Further, to avoid CSF-contaminated voxels in the registered cortical masks, we performed a mask-segmentation optimization using a semi-quantitative T2-map, as previously described [MRI scans were obtained from a 3 Tesla scanner  with a 12-channel head coil. The MRI study protocol included a T1-weighted 3D sequence with magnetization prepared raid gradient echo (MPRAGE) with whole brain coverage, 1mm isotropic resolution, 1900 ms repetition time, 2.19 ms echo time, 900 ms inversion time and 9° flip angle for subsequent automated tissue segmentation. R2* relaxation data were acquired using a spoiled 3D multi-echo gradient echo sequence (FLASH) with 1x1x2 mmlgorithm , which tard.edu) , 55 was ard.edu) , 57. Theescribed . The medThe APOE risk factor was included as APOE ε4 carriers versus non-carriers and smoking was defined as current or former smoker versus non-smoker. Hypertension was considered as history of hypertension and/or systolic blood pressure over 140 mmHg or a diastolic blood pressure over 90 mmHg and/or current use of antihypertensive agents . Diabetehttp://solar-eclipse-genetics.org). SOLAR decomposes the phenotypic variance (Vp) of R2* iron into polygenic variance (Vg) and environmental variance (Ve) [p is the residual phenotypic variance which cannot be explained by the covariates. Covariates are modelled as fixed effects and the phenotype is residualized on these covariates. Vg is the variance due to additive effects of genes based on pedigree relationships. The pedigree structure is described by a kinship matrix which includes the amount of shared genes between each pair of individuals according to their relationship. For instance, sibling pairs and parent-child pairs share 50% of their genes while avuncular and half-sibling pairs share 25%. Ve is the variance due to environmental factors, the non-additive genetic components, and measurement errors. The variance components are estimated by comparing the observed phenotypic covariance matrix with the covariance matrix predicted by kinship, and the heritability is calculated as h2=Vg/Vp. Significance of the heritability is tested by comparing the likelihood of the model in which Vg is constrained to zero with that of a model in which Vg is estimated. The variance component models can be extended to bivariate analyses to determine if two phenotypes are influenced by the same genes. The phenotypic correlation (rp) between two quantitative traits can be partitioned into a genetic and an environmental component using the kinship information. Likelihood ratio tests are used to test if the genetic (rg) or the environmental (re) correlation is different from zero.Descriptive Statistics were calculated with the SPSS software . Heritability of R2* iron in the cortex and basal ganglia was estimated with variance components models as implemented in the SOLAR software  . Vp is thttps://cran.rstudio.com/web/packages/ggseg3d/ggseg3d.pdf), fsbrain  and qgraph  in R .We estimated the heritability of R2* iron in the basal ganglia and in the cortex using a basic model including age and sex as covariates. Extended models including age, sex and additionally either APOE ε4 carrier status, BMI, diabetes, hypertension, hypercholesterolemia or smoking were calculated to determine the proportion of variance in R2* iron due to lifestyle factors. Covariate screening was used to determine the statistical significance of each covariate effect and only significant covariates (p<0.1) were kept in the model. The significance of each covariate in the model was tested using a likelihood ratio test comparing the models with and without covariates. A liberal significance threshold of 0.1 was chosen to avoid the removal of relevant covariates from the model. The proportion of variance due to APOE ε4 carrier status, BMI, hypertension, diabetes, hypercholesterolemia or smoking was calculated by subtracting the proportion of variance due to all significant covariates from the model including the respective covariate from the proportion of variance due to covariates of the basic model. We also estimated the genetic correlation between R2* iron in the basal ganglia and cortical lobes with age and sex as fixed covariates. Since variance component methods as implemented in SOLAR are sensitive to deviations from the normal distribution , we applData sets generated and/or analyzed during the study are available from the corresponding author upon reasonable request.


Inner Mongolia Arbas white cashmere goats is a dual-purpose breed for producing cashmere and meat. In recent years, its meat has become more and more popular among consumers because of rich nutrients and delicious flavor. Therefore, it is particularly important to study the genetic and non-genetic factors affecting the early growth traits and estimate variance components of pre-weaning growth traits of Inner Mongolia Albas white cashmere goats. A total of 37487 kidding records such as birth weight (BWT), weaning weight (WWT), average daily gain from birth to weaning (ADG) and Kleiber ratio (KR) from 343 sires and 7296 dams were used in this study. The most appropriate model was chosen on the basis of likelihood ratio test by fitting six models which excluding or including maternal genetic, maternal permanent environmental effects. The parameters were estimated under the most appropriate model using AIREML method by WOMBAT software. With the best model (Model 6), heritability estimates were 0.0435, 0.0911, 0.0932 and 0.2339 for BWT, WWT, ADG and KR traits, respectively. Maternal heritability estimates were 0.0143, 0.0246, 0.0220 and 0.0186 for BWT, WWT, ADG, and KR traits respectively. The correlation between different traits was estimated with the most suitable model by using bivariate analysis method. The direct additive genetic correlation among the traits ranged from −0.026 (BWT~KR) to 0.772 (ADG~KR). The maternal permanent environment correlation is between −0.289 (BWT-KR) ~0.900 (WWT-ADG). Results indicated that maternal effects and direct-maternal genetic covariance should be considered in any program aimed at improving pre-weaning growth traits to have an accurate genetic evaluation. In addition, positive and medium to high genetic correlations generally exist among WWT, ADG and KR due to the existence of genetic variation for early growth traits. The results showed that the genetic progress of these traits could be slowly through selection except for KR. As is well-known, goats are important multipurpose economic animals. Cashmere goat is a breed mainly raised for cashmere production. Cashmere goats live primarily in the arid and semi-arid areas of China  is a dual-purpose breed for producing meat and cashmere, which is mainly distributed in Ordos, Inner Mongolia, China. Traditionally, IMWACGs mainly produced high-quality cashmere, supplemented by meat production. In the last decade, cashmere profits have fallen whereas mutton profits have gradually increased. Goat meat is valued as a high-protein and low-fat food that contains fatty acids and amino acids necessary for the human body . TherefoAn understanding of the influencing factors and the genetic principles affecting the growth traits is necessary to implement optimal breeding and selection programs . PreviouEarly growth is influenced by several factors, including environmental variables such as herd, grassland quality, direct genetic effects of the kid, and the maternal additive genetic and permanent environmental effects of the dam . SeveralThis study aimed at estimating the genetic parameters for preweaning body weight traits and Kleiber ratio and also the genetic correlations among these traits to consider these traits in the genetic selection program.x indicates the observational value of traits) and records from animals from an unknown dam were omitted. Finally, 37,487 kidding records from 343 sires and 7,296 dams were used in this study. The kids were weighed and ear-tagged at birth and weaned for about 90 days. The kids were fed with hay freely during the day and suckled with the grazed dams during the night until 30 days of age. 2 months after birth, the kids suckled their mothers two times a day, morning and evening, until weaning. The maiden does were mated by artificial insemination at about 18 months of age. In the present study, the birth weight (BWT), average daily gain from birth to weaning (ADG), weaning weight (WWT), and preweaning Kleiber ratio (KR) were analyzed. The Kleiber ratio is an indirect selection method for measuring feed conversion. The data used in the analysis are given in The IMWACG population is mostly raised in Ordos , the environmental details of which have been described by Zhou et al.  and Bai The fixed effects of each trait were determined by a general linear model (GLM) procedure using SAS software . The fixyijklmn is the observation of nth kids that belonged to the ith year, the jth herd, the kth type of birth, the lth sex, and the mth dam age. μ is the overall mean, (PH)ij is the interaction between the ith year and the jth herd, (TS)kl is the interaction between the kth type of birth and the lth sex, and eijklmn is the random error.where −8. The models are as follows:The (co)variance components were estimated for each trait by the restricted maximum likelihood (REML) method using the WOMBAT program . Six dif1, Z2, and Z3 are the design matrices associating the fixed effects, the direct additive genetic effects, the maternal additive genetic effects, and the maternal permanent environmental effects to the y vector. For WWT, we also considered the days between birth to weaning as the covariate for quadratic regression in the model.where y is a vector of observations on the different traits; and b, a, m, c, and e are the vectors of the fixed effects, the direct additive genetic effects, the maternal additive genetic effects, the maternal permanent environmental effects, and the residual effects, respectively. X, ZA is the additive genetic correlation matrix, Id and In are the identity matrices that have an order equal to the number of dams and kids, respectively, and σam denotes the covariance between the direct additive genetic and the maternal additive genetic effects.The direct additive genetic effects, the maternal additive genetic effects, the maternal permanent environmental effects, and the residual effects are assumed to be normally distributed with mean 0 and To create the most suitable model for the traits, a likelihood ratio test (LR) was performed to obtain the appropriate model as given below :1 and L2 are the maximum likelihood function values of model 1 and model 2, respectively. Model 1 is a sub-model of model 2. LR obeys the chi-square distribution. The degree of freedom is the number of parameters considered in model 2 minus the number of parameters in model 1.where LFurthermore, the genetic and phenotypic correlations between the traits were obtained using bivariate animal models based on the most appropriate model for each trait.From 2) estimated by model 1 were the highest. With the inclusion of the maternal genetic effect, the maternal environmental effect, or the individual additive and maternal additive genetic covariance in other models, the direct additive genetic variance and heritability estimates decreased.The variance groups of BWT, WWT, ADG, and KR estimated by the six models are listed in The likelihood ratio test results of the different models are listed in The variance components and genetic parameters of different models for each trait were estimated and are shown in It was estimated that the correlation of WWT heritability is between 0.0841 (model 4) and ~0.2737 (model 1), maternal heritability is between 0.0246 (model 6) and ~0.0752 (model 3), the variance ratio of the maternal permanent environmental effects is between 0.0272 (model 6) and ~0.0509 (model 2), and that of direct genetic effect with maternal heritability is 0.6396 (model 4) and 0.6710 (model 6). They have a high positive correlation.The ADG heritability estimated by different models was between 0.0856 (model 4) and 0.2776 (model 1), the maternal heritability range was between 0.0220 (model 6) and 0.0713 (model 3), and the variance ratio of the maternal permanent environmental effects was between 0.0246 (model 6) and 0.0467 (model 2). There was a highly positive correlation between the direct additive effect and maternal genetic correlation, which was estimated to be 0.7497 by model 4 and 0.7906 by model 6.The KR heritability estimated by different models was between 0.2080 (model 3) and ~0.3075 (model 1). The variance ratio of maternal heritability to maternal permanent environmental effect was low, ranging from 0.0168 (model 5), ~0.0501 (model 4), 0.0308 (model 5), and ~0.0396 (model 2), respectively. The direct additive effect had a low negative correlation with the maternal genetic effect, ranging from −0.0103 (model 4) to approximately −0.0874 (model 6).As presented in The correlation between different effects was estimated with the most suitable model of traits using the bivariate analysis method. The results are shown in The phenotypic correlations between BWT and other traits were both low negative and positive correlations  and the phenotypic correlations between other traits were highly positive correlations .p < 0.01) in the present study and were in agreement with the previous reports on Sirohi goats , which was consistent with that reported by Menezes et al.  for BoerThe heritability, maternal heritability, and direct maternal genetic correlation of the same traits between breeds were different. The main reason may be that the breeds were different, and their breeding directions and schemes were also inconsistent, resulting in different micro-effect polygenes affecting the quantitative traits carried inside or outside the nucleus of each breed, thus leading to the genetic diversity of the same quantitative trait among different breeds.Estimates of different correlations between the investigated traits are shown in The WWT had a positive and medium genetic correlation with ADG (0.614). Zhou et al.  reportedHowever, the maternal genetic correlation was negative between WWT and KR but positive among other traits. The BWT-WWT (0.388), BWT-ADG (0.406), and WWT-ADG (0.577) maternal genetic correlation was medium, while it was high for ADG-KR. The results were significantly lower than those of Dige et al.  for JamuThe different genetic correlations between the same traits of different breeds and between different traits of the same breed can be attributed to the following aspects: first, the genes that affect the quantitative traits are mostly micro-effect polygenes that are vulnerable to environmental effects. Different breeds have different breeding environments and selection pressures, resulting in differences in gene expression although they carry the same genes; therefore, the genetic effects of various traits are different in different breeds. Second, according to the principles of genetics, the mechanisms of genetic correlation are pleiotropism and linkage inheritance. Therefore, the number of common genes, the genes for different traits, and the strength of linkage between the genes are the main reasons for the difference in genetic correlation between traits. Finally, the models, data volume, and estimation methods used in statistical analysis are also some important reasons for the differences in parameter estimation between the same traits of different breeds and different traits of the same variety.The estimates of maternal permanent environmental correlation range from −0.366 (BWT-KR) to 0.957 (WWT-ADG) among early growth traits. The estimates of environmental correlation among WWT, ADG, and KR were positive and high and range from 0.739 for WWT-KR to 0.957 for WWT-ADG, which were probably due to the greater similarity of the environmental and management conditions. The differences in uterine space during pregnancy, nutrients during pregnancy and lactation, maternal behavior, and the mothering ability of the dam may be the main reasons for the variabilities in maternal permanent environmental effects.The phenotypic correlation estimates among WWT, ADG, and KR changed from −0.294 for BWT–KR to 0.927 for WWT–ADG, which were in agreement with the estimates of environmental correlation for the same traits. The phenotypic correlation estimates between the traits indicated the presence of a desirable association among pre-weaning traits.These results indicated that environmental factors have a significant impact on pre-weaning growth traits and hence should be considered while accurately modeling genetic evaluation. The low-to-moderate estimates of direct heritability for pre-weaning growth traits indicated the presence of genetic variabilities within the IMWACGs, and the genetic progress of these traits can be achieved by selection.The maternal genetic effects and the maternal permanent environmental effects seemed to have a significant effect on early growth traits. Thus, in addition to the direct genetic effect, the maternal genetic effects and maternal permanent environmental effects were found to be important for the genetic evaluation of early growth traits in this breed. The moderate-to-high estimates of genetic correlation among WWT, ADG, and KR indicated that the selection of one of these traits would probably bring out a positive response for the selection from the correlated one. Therefore, selection for early growth traits should only consider one of them when designing a breeding program for this variety.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.All animal experiments were performed in accordance with the Guidelines for Experimental Animals of the Ministry of Science and Technology  and were approved by the Scientific Research and Academic Ethics Committee of Inner Mongolia Agricultural University and the Biomedical Research Ethics of Inner Mongolia Agricultural University . Written informed consent was obtained from the owners for the participation of their animals in this study.RW, YLiu, and JL conceived the idea and designed the study. RW, ZW, YS, YQ, and YLi analyzed the data. RW and YLiu wrote the draft. YZhan, RS, and YZhao finalized the manuscript. All authors read and approved the final manuscript.


Ovis aries) and argali (Ovis ammon). We discovered one novel locus associated with viability and two novel loci associated with meat productivity. For loci associated with meat productivity, we demonstrated the effect of negative heterosis. These results may help to understand one of the possible genetic mechanisms of negative heterosis.One of the main aims of selection is enhancing the economically important traits of animals and plants. Crossbreeding is one of the common ways to improve the economically important traits. Usually, different species of one genus are used for crossbreeding. The use of different closely related species is known as hybridization. It is expected that hybrids will have advantages over their parents in terms of economic productivity due to the effect known as heterosis. However, the phenomenon of negative heterosis exists and can induce negative impacts on different important traits, such as viability, reproductivity, productivity, etc. The exact biological mechanisms of negative heterosis remain unknown. In this study, we aimed to determine the genetic factors associated with negative heterosis in interspecific hybrids between domestic sheep (Ovis aries) and argali (Ovis ammon). One locus (rs417431015) associated with viability and two loci  associated with meat productivity were identified. One gene (ARAP2) was prioritized for viability and three for meat productivity . The loci associated with meat productivity were demonstrated to fit the overdominant inheritance model and could potentially be involved int negative heterosis mechanisms.Negative heterosis can occur on different economically important traits, but the exact biological mechanisms of this phenomenon are still unknown. The present study focuses on determining the genetic factors associated with negative heterosis in interspecific hybrids between domestic sheep ( Ovis aries) with their wild ancestors, such as the Asian mouflon  [Ovis ammon polii) [Ovis orientalis musimon) [Ovis canadensis) [Mannheimia haemolytica in the absence of vaccination [Producing new breeds is an essential process for increasing the phenotypic indices of economically important traits, and hybridization is one of the ways to do so. Usually, different species of one genus are used for hybridization, but in some cases different closely related species are used. For example, in sheep genetics there are a few examples of hybridization of domestic sheep  , Pamir an polii) , Europeamusimon) , and bigadensis) . It is eadensis) . Hybridiadensis) , while hcination .Negative heterosis, on the other hand, can have negative effects on different important traits, such as viability, reproductivity, productivity, etc. Unfortunately, despite its economic importance, this phenomenon remains understudied.In quantitative genetics there are several models of heterosis. The genes causing this phenomenon may affect traits in three different ways: dominance  ,7,8, oveAs for domestic animals, the majority of studies on negative heterosis and its possible genetic and cytogenetic mechanisms have been conducted on chickens ,17. ThesOvis aries) and argali (Ovis ammon) [Ovis aries) and argali (Ovis ammon).Previously, our group focused on obtaining a population of hybrids between domestic sheep (s ammon) . In thisA total of 89 hybrids of F1 Romanovskaya × argali backcross were analyzed. Handling and breeding of the Russian sheep population followed the protocols approved by the Ethics Committee on Animal Care and Use of the Institute of Cytology and Genetics .® HD SNP BeadChip containing 603,350 (HD) SNPs . The alleles were coded using the Illumina A/B scheme. Quality control of the hybrid genotypes was performed using information about four additional groups of animals . Earlier, some SNPs had been noted to have a very low quality of genotypes in each of these groups. These SNPs were removed from the analyzed hybrid genotypes to prevent methodological errors. Then, a standard quality control procedure was carried out, according to the following SNP and sample inclusion criteria: SNP and sample call rate > 0.98; sample heterozygosity within ±3 standard deviation ranges; and minor allele frequency (MAF) > 0.01. As a result, 87 animals and 446,790 SNPs passed the quality control.The animals were genotyped using the Ovine InfiniumTo calculate a kinship matrix, a subset of the pruned SNPs that were in approximate linkage equilibrium with one another was generated using such options as maf 0.05—indep-pairwise 500 kb 10.3 from PLINK 1.9 . In totaThe kinship matrix was calculated using the IBs function from the R package GenABEL .Eleven carcass traits see  were meaIf an animal had no phenotypic information starting from a given time, it was considered to be deceased by that timepoint. The numbers of living animals for each timepoint are presented in p-value for one-degree association tests for additive, recessive, dominant, and overdominant models. The test was applicable only for the SNPs with all three genotypic classes available. If an SNP had only two genotypic classes, a one-degree-of-freedom test without inheritance model inference was applied.For both viability and meat productivity traits, an association test with two degrees of freedom was carried out, which did not restrict the locus inheritance model. The probable inheritance model was assessed by either association boxplots or by the lowest-association p-values based on the following procedure. The null model (H0) was set as follows:p-value significance threshold was determined as follows:We used the Cox regression model from the R package SURVIVAL. The likelihood ratio test (LRT) was applied for estimating association For a locus that passed the threshold, Cox regression using the mlreg function from the R package GenABEL was applied. This analysis was conducted for the four inheritance models .p-value significance threshold value was set as follows:For this study, a mixed linear regression model was applied. The GRAMMAR-Gamma method was used to correct the phenotypes in relation to their relatedness structure . To perfThe associated loci were defined as regions within ±500 kb around the SNPs most significantly associated with the trait (lead SNPs). A locus was defined as new if it was not present on the list of known SNPs and genes from our previously published database of the QTLs and genes associated with meat productivity in sheep . This daInflation factors for five traits are presented in The Ensembl Variant Effect Predictor (VEP) was applied to analyze the potential effects of the SNPs and indels that were in high linkage disequilibrium  with the lead SNPs. The analysis was performed using the software available online .For estimation of additive heritability, we used GREML analysis from GCTA software . Sex, nuFor replication analysis, different types of hybrids of the Romanovskaya breed and Katahdin breed with argali and mouflon  were used . We perfp-value  were prioritized. PDE2A encodes the phosphodiesterase 2A protein, and its mutations are associated with movement disorders in humans, such as chorea [ARAP1 is involved in the regulation of membrane trafficking and actin cytoskeleton reorganization through activation of Arf and Rho GTPases [ARAP1 is known as a common regulatory variant for type 2 diabetes in humans [PCDH15 gene cause Usher syndrome type 1F in humans [As for the two loci associated with meat productivity (rs413302370 and rs402808951), three genes . This may be related to the observed non-additive effects of SNPs associated with the compactness index and the phenomenon of negative heterosis. The overdominant model indicated the classic pattern of a genotype associated with a trait affected by heterosis, i.e., the heterozygotes showed substantially inferior performance compared to the homozygotes B,C. The Ovis orientalis musimon), the hybrids had less weight than domestic sheep [Ovis ammon polii) [PCDH10 as a candidate gene associated with body weight traits. In our study, we prioritized the PCDH15 gene for the compactness index, which belongs to the same protein family as PCDH10. Further investigations of these proteins are needed to understand the possible mechanisms of influence on meat productivity traits.Unfortunately, there are only a few examples of studies of interspecific sheep hybrids in the literature. To the best of our knowledge, there have been no studies describing the phenomenon of negative heterosis in interspecific hybrids. Our results showing negative heterosis contradict the findings of a previous study that showed increased lean meat production for interspecific sheep hybrids with Asian mouflon , which cic sheep . Our resn polii) . In thatIt should be noted that there have been a few sheep studies where heterosis was shown and measured. The effects of direct heterosis  were small for an Australian population including more than 1 million animals . In anotARAP1 and ARAP2) were prioritized. Both of these genes are involved in membrane transport: ARAP1 regulates the cell-specific trafficking of a receptor protein involved in apoptosis, while ARAP2 encodes a protein that is associated with focal adhesions and functions downstream of RhoA to regulate focal adhesion dynamics. Presumably, there is a gene network including these genes, but additional investigation into interspecific sheep hybrids is required to clarify this issue.It is noteworthy that for both viability and meat productivity the genes from one protein family . For that reason, we do not expect that our findings could be replicated using other populations, considering that the possible heterosis mechanisms could be unique for each hybrid population. We performed replication analysis using other types of hybrids . No SNPs reached the significance threshold of replication.Better understanding of the genetic causes of negative heterosis in different domestic animals may help to improve breeding results. Knowledge of “proper” heterosis-associated allele combinations may help us to develop efficient marker-assisted systems that can be applied for breeding purposes.In summary, the findings of the present study suggest two possible genetic markers (with three prioritized genes) that could potentially be involved in negative heterosis mechanisms in interspecific hybrids between domestic sheep and argali. In addition, we discovered one novel locus that was associated with viability. Further investigation is needed to clarify the possible molecular role of the discovered genes in negative heterosis. The results of the present study can help researchers and breeders in futures studies to create new interspecific sheep hybrid populations that are more successful in terms of economically important traits, as well as to develop efficient marker-assisted systems for breeding purposes.


Individuals with South Asian ancestry have a higher risk of heart disease than other groups but have been largely excluded from genetic research. Using data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort, we conducted genome-wide association studies of coronary artery disease and its key risk factors. Using power-adjusted transferability ratios, we found evidence for transferability for the majority of cardiometabolic loci powered to replicate. The performance of polygenic scores was high for lipids and blood pressure, but lower for BMI and coronary artery disease. Adding a polygenic score for coronary artery disease to clinical risk factors showed significant improvement in reclassification. In Mendelian randomisation using transferable loci as instruments, our findings were consistent with results in European-ancestry individuals. Taken together, trait-specific transferability of trait loci between populations is an important consideration with implications for risk prediction and causal inference. Most genetic studies of disease have been done in European ancestry cohorts, and the relevance to other populations is not guaranteed. Here, the authors use data from 22,000 British South Asian individuals and find that the transferability of polygenic scores was high for lipids and blood pressure, and lower for BMI and coronary artery disease. Understanding the determinants of excess CAD burden in South Asian populations and improving prediction to enable preventive interventions to represent important public health priorities.Individuals with South Asian ancestry account for more than a fifth of the global population and experience a higher risk of coronary artery disease (CAD) than other ancestries. For example, British South Asians have three- to four-fold higher CAD risk than White British people2.Common genetic variation is an important determinant of CAD and of upstream risk factors, such as blood pressure, lipids, and body mass index (BMI). The genetic component of disease risk can be harnessed to identify underlying disease genes and pathways, to estimate the unconfounded effects of risk factors by Mendelian randomisation, and to improve risk prediction through the application of polygenic scores (PGS). However, the genetic basis of CAD risk is not well characterised in South Asian ancestry populations because genome-wide association studies (GWAS) have been mostly limited to European ancestry populations6, the extent to which this attenuation is due to genetic drift  versus heterogeneity of causal genetic effects remains unclear.Fundamental questions remain about the extent to which the genetic determinants of cardiometabolic traits are shared by European and South Asian ancestry populations. These have important implications for translational applications of genetic data, such as causal inference with Mendelian randomisation which could prioritise different prevention strategies or drug targets between ancestries, and clinical risk prediction. Whilst the predictive performance of PGSs derived from European ancestry populations in other ancestry groups decreases with genetic distance10. Secondly, there is evidence that the performance of PGSs may be modified by factors, such as educational attainment and socioeconomic status for which research studies are often not representative12. The robustness of PGSs applied to South Asian-ancestry individuals in a real-world healthcare system is largely unknown.Furthermore, most previous large-scale studies assessed genetic risk prediction using data from a research setting. These findings may not generalise well to a real-world clinical setting. Firstly, clinical risk factors may be measured less comprehensively than in a research setting, affecting the performance of integrated risk models combining these factors with PGSs13. G&H is a community-based cohort of British Pakistani and Bangladeshi individuals with linked electronic health record data . This unique cohort represents an understudied and clinically vulnerable population with high levels of socioeconomic deprivation, and this is, to our knowledge, the first major genetic study focused on it. We apply new approaches to the transferability of genomic risk loci across populations, perform ancestry-specific and trans-ancestry Mendelian randomisation analysis, investigate the transportability of PGSs for CAD and its risk factors, and estimate the incremental improvement in CAD prediction when incorporating the CAD PGS into clinical risk tools.Here, we perform a comparative analysis of the genetics of CAD and upstream cardiometabolic traits in European and South Asian ancestry populations, using data from the Genes & Health (G&H) cohortN = 1110) of the individuals had coronary artery disease (CAD), with the age of onset ranging from 17 to 97 years old (median 55). A quarter of the G&H participants were on active statin prescriptions, 23% on BP medications, 29% had high TC levels (>5 mmol/L), and 30% had high LDL-C levels  in G&H, with estimates similar to those in eMERGE . We observed high genetic correlations between G&H and European ancestry samples from UKBB for all traits, with the lowest value seen for SBP .We compared the genetic architecture of CAD and upstream risk factors, namely HDL-C, LDL-C, triglycerides (TG), total cholesterol (TC), systolic and diastolic blood pressure (SBP & DBP), between British Pakistanis and Bangladeshis from G&H, and European ancestry individuals from the electronic health record-based eMERGE cohort, since phenotypes had been ascertained in a similar way. All traits were found to have significant SNP heritability . To explore whether this was likely to be due to ancestry differences or other factors, we also calculated the PAT ratio in eMERGE, and observed a similarly low PAT ratio for CAD  , highlighting the importance of accounting for power when assessing transferability. We report the observed number divided by the expected number of loci and call this new approach the power-adjusted transferability (PAT) ratio. Across most traits examined, the observed number of transferable loci closely matched the loci we expected , but not with LDL-C levels (p = 0.23) . Out of a total of 184 well-powered loci tested across all traits, only nine were non-transferable; that is, no variant in the credible set was significant at 0−3 Fig. . These n23) Fig.  despite 14. To assess the extent of sharing of causal variants between ancestries at previously reported loci with evidence of transferability, we applied trans-ancestry colocalisation for G&H with UKBB European ancestry samples as the reference. Colocalisation methods can estimate the likelihood of causal variant sharing without the need to identify the specific causal variant. We found evidence for the most extensive sharing of causal variants for transferable lipid loci: total cholesterol , followed by TG (56%), HDL-C (48%), and LDL-C (47%) (Table PCSK9 were among variants that were consistently not shared (pJLIM > 0.05) between the two populations  Table . Causal ons Fig. .R2” statistic for continuous risk factor traits; these are the gain in AUC or R2 when adding the PGS to the regression of phenotype on the baseline covariates .To assess the performance of PGSs for cardiometabolic traits derived from European ancestry populations in British Pakistani and Bangladeshi individuals, we compared predictive performance in G&H to that in European ancestry individuals from eMERGE. We quantified predictive accuracy using the “incremental AUC” statistic for CAD and the “incremental 15 and European ancestry samples16, respectively. PGSs for all phenotypes assessed were significant predictors of their target trait in G&H , but it was higher for lipids and BMI, ranging from 3.9% to 6.7%. Relative accuracy of PGS in G&H versus eMERGE, determined by the ratio of incremental AUC or R2, was close to 1 for HDL-C, TG, SBP, and DBP, and lower for CAD  and BMI  to 6.2% (5.3–7.1%) when using statin-adjusted LDL-C in G&H .We first evaluated previously published PGSs from the PGS Catalog  method and optimised them separately within G&H and eMERGE using 10-fold cross-validation. The numbers of SNPs in the most frequently selected best C + T PGSs are similar between eMERGE and G&H, and PGSs for lipids contained fewer SNPs (194–454) than other traits   Fig. . This ma15 , showed the highest predictive accuracy in British Pakistani and Bangladeshi individuals in G&H. This score had an OR per SD of 1.63 (95% CI: 1.51–1.76) and incremental AUC of 0.009 . The integrated score combining QRISK3 and the CAD PGS had a non-significant improvement in the C-index  but a significant improvement in reclassification  using a 10-year risk threshold of 10% based on the threshold for preventive intervention with statin treatment recommended by National Institute for Health and Care Excellence20. The integrated score reclassified 3.2% of the population as high risk and 2.5% as low risk  of 0.843 , higher LDL-C  and lower HDL-C levels  were associated with increased risk of CAD in G&H. The effects for SBP and DBP were not statistically significant in G&H. However, both had relatively small numbers of loci as instruments and confidence intervals of the effect estimates were wide.We carried out two-sample Mendelian randomisation analyses to estimate potentially causal effects of the risk factors on CAD in G&H and compared findings with European ancestry samples from eMERGE. For G&H, we used transferable loci as genetic instruments to benefit from the precision of largely European ancestry discovery GWAS whilst ensuring only valid instruments are used. In eMERGE, estimates of causal effects for BMI, BP, and lipids, except TG, were statistically significant Fig. . Consistp < 5 × 10−8) were identified  for selecting instruments. For the lipid biomarkers, the results were consistent regardless of which loci were chosen as instruments. However, the association of BMI with CAD was significant only for transferable loci , LDL-C when using the UKBB South Asian ancestry-ascertained loci  and HDL-C for transferable loci . However, the results of the weighted median and weighted mode models were consistent with those obtained by the inverse-variance weighted Mendelian randomisation model  and CAD also had fewer transferable loci. A CAD PGS optimised for South Asian ancestry individuals nonetheless yielded an appreciable improvement in risk reclassification  when combined with the QRISK3 clinical risk score.23. A recent study compared genetic determinants of >200 lipid metabolites in 5000 South Asians from Pakistan and 13,000 Europeans and found high overlap in the detected associations24. Using a new method, our paper goes further by empirically demonstrating that, in most cases where loci do not replicate, it is due to the lack of power. These findings suggest that, in large part, the genes and pathways that influence the tested cardiometabolic traits are shared between these ancestrally divergent populations. One surprising finding was that the major LDL-C locus at CETP was not associated with this biomarker in G&H but exhibited pleiotropic effects on HDL-C. This is consistent with the observation from a recent study in ancestrally diverse individuals25. Abnormalities in CETP are linked to accelerated atherosclerosis and might play an important role in increasing risk in South Asian ancestry individuals26.Other genetic studies of CAD and related traits that have evaluated reproducibility of loci in South Asian ancestry populations have either been limited by small sample sizes or have restricted their comparisons to the index SNP identified in the GWAS, which does not take LD or statistical power for replication into accountp-value = 1.6 × 10−5), probably because statin-adjust data were used in the discovery GWAS but lipid-lowering medication data were not available in eMERGE. For CAD, eMERGE had more cases (6815 vs. 1110), and we were able to replicate 31 loci (9 in G&H). Nevertheless, this is significantly lower than what would be expected given the replication power for eMERGE. In fact, the PAT ratio  was similar to G&H . The lower PAT ratio for CAD in both G&H and eMERGE may indicate that it is a more complex outcome than measurable continuous biomarkers and the definition of CAD is potentially affected by cohort-specific factors such as how the diagnosis is coded in different health care systems. Procedure codes were not available in eMERGE and thus were not considered in defining CAD cases, which adds another source of uncertainty to the comparison.For benchmarking we also assessed the transferability of genetic loci in European-ancestry participants from eMERGE. For HDL-C, triglycerides, and blood pressure, the PAT ratio was close to one Table . For LDL28. Consistent with this, the estimated causal effect of BMI on CAD was significant only when using the transferable loci as instruments in the Mendelian randomisation analysis. Visceral adiposity is a strong risk factor for cardiometabolic diseases, independent of total fat mass; these findings warrant further study and may suggest that BMI may not be an optimal biomarker of adiposity in South Asian ancestry individuals29.BMI had the lowest proportion of transferable loci with shared causal variants as well as lower accuracy of the PGS in G&H relative to samples of European descent in eMERGE and a genetic correlation nominally significantly lower than one. South Asian ancestry individuals are known to have higher visceral fat at the same BMI compared to European ancestry individuals in Western countries31, PGSs for HDL-C and triglycerides had similar predictive accuracy between the two ancestry groups. It is perhaps not surprising to observe a reduction in the accuracy of the CAD PGS in BPBs, given that it did not transfer well even within European-ancestry subpopulations32. Medication data were not available in eMERGE, we thus compared the PGS accuracies using the unadjusted, “highest ever” measurements for LDL-C and blood pressure. In G&H where medication data were available, PGSs for SBP and DBP showed similar performance in predicting the adjusted and unadjusted values , and because the relative accuracy also depends on differences in allele frequencies and LD patterns at these SNPs between populations, which we have not factored in and may differ between traits.We explored the factors that may impact the relative accuracy of PGSs. Based on a recently proposed theory, relative accuracy is proportional to the product of the trans-ancestry genetic correlation and the ratio of heritability estimates33. Incorporating UKBB South Asian ancestry GWAS data in meta-PGSs proposed by Marquez-Luna et al. 33 and using PRS-CSx19 did not show a large improvement in G&H. A likely reason is the limited sample size of the South Asian ancestry samples in UKBB for some of the traits. Larger samples of South Asian ancestry individuals are needed to examine if ancestry-matched GWAS data can improve prediction accuracy over and above what would be expected from the increased sample size. The increased value of increasing European-ancestry samples versus diversifying ancestries in GWAS will depend on the extent to which the causal variants are shared. For traits for which the causal variants are shared, there is more to be gained from more powerful European ancestry GWASs, even without adding samples of the target ancestry. However, increasing diversity in GWASs will greatly improve the resolution of fine-mapping and the power to identify the causal variants by leveraging the LD differences across ancestries34.Several groups have shown improvements in PGS performance in diverse ancestry groups when incorporating summary statistics from ancestry-matched samples35; our study represents an important addition since it captures the noise with which QRISK3 is actually measured within a real-world clinical setting (as opposed to using comprehensive measures taken for research purposes), which may affect the performance of integrated risk models combining these factors with PGSs. We note that only about 4% of the ~8 million individuals used for developing QRISK3 were of South Asian ancestry36, and the weights for each conventional risk factor might not be optimal for South Asian ancestry individuals. QRISK3 was developed to predict cardiovascular disease (CVD), which is a composite outcome of CAD and stroke. However, our analysis focused on CAD, which is an important component of CVD and the main focus in GWASs and genetic prediction studies. The PGS for CAD developed by Wang et al. showed robust association with CAD in G&H, with a similar OR per SD in PGS  as in their study 15. The integrated score combining PGS and QRISK3 showed significant reclassification improvement against QRISK3 alone (NRI 3.9% (95% CI: 0.9–7.0%)). Previous studies in UKBB European ancestry samples reported similar improvement, with NRI estimates of 3.5% (95% CI: 2.4–4.5%)8 and 3.7% (95% CI: 3.0–4.4%)35 in two different analyses using CAD as the outcome. However, these NRI estimates are probably affected by using UKBB samples that are healthier than the general UK population without recalibrating risk to a primary care setting9. In G&H, the PGS improved the identification of high-risk individuals in people younger than 55 years as well as low-risk individuals in people older than 55 years, both of which are important in a clinical setting. We anticipate that, like European ancestry individuals35, the British Pakistani and Bangladeshi community  would also benefit from the use of integrating PGS in primary prevention settings.We assessed the clinical value of the PGS for CAD on top of the traditional clinical risk factors captured in the QRISK3 algorithm. Similar work has been done previously in research cohortsMendelian randomisation has emerged as a powerful tool to explore the potential causal effects of risk factors on disease outcomes. Statistical power can be the limiting factor when extending these analyses to ancestrally diverse populations because independent ancestry-matched GWAS for risk factors of interest may not be sufficiently large. To increase power to estimate the MR causal effects of risk factor traits on CAD in British Pakistanis and Bangladeshis, we used genetic instruments derived from large European ancestry GWAS. Some of the loci may be invalid instruments for other populations. However, restricting the published loci to the ones that were transferable in this population successfully addressed this issue for BMI and shows promise as a new approach for trans-ancestry Mendelian randomisation. An assumption that requires further study is whether the effect sizes of transferable loci are the same for each ancestry group. Future research should also further investigate the impact of pleiotropy on the causal effect estimate for HDL-C.9. Secondly, for the comparisons of results in G&H with other cohorts, it should be noted that each of the cohorts examined here is unique. We selected eMERGE, which is also based on electronic health records, for comparison with G&H. Although UK Biobank would have been a better match in terms of country, we were unable to use it for comparison because it was one of the studies included in the published GWAS meta-analyses for the cardiometabolic traits that formed the source of locus discovery as well as the PRS training data. Differences in ascertainment (including the age distribution) and clinical measurements within different cohorts and healthcare systems may have impacted the genetic associations. Different genotyping arrays and imputation panels of different sizes and ancestries were used in G&H and eMERGE, which might lead to potential bias in the comparisons of the two cohorts. The lack of medication data in eMERGE meant we were limited in the conclusions we could draw from comparisons of heritability, transferability (PAT ratio), and PGS performance for LDL-C and blood pressure data. G&H is enriched for young people (median age 40 years old), given that the median age of onset for CAD is 55, some young individuals in this cohort will develop CAD beyond the observation period, which might lead to the underestimation of accuracy and clinical value of the CAD PGS. Finally, our transferability analyses evaluated whether there is evidence for a directionally consistent association in G&H but we did not assess heterogeneity in effect sizes which would also impact genetic risk prediction.Our study has several limitations. Firstly, due to the limited sample size in each age-by-sex subgroup, we could not recalibrate risk prediction models in G&H to what would be expected in an unbiased primary care settingIn conclusion, our work provides the first comprehensive assessment of the transferability of cardiometabolic loci to a non-European ancestry population and its impact on two key applications of genetics, causal inference and risk prediction. Our protocol and our new approach for transferability can serve as methodological standards in this developing field. We have shown high transferability of GWAS loci across several cardiometabolic traits between European ancestry and British Pakistani and Bangladeshi populations. The performance of PGSs is trait-specific. Our results suggested there would be clinical value in adding PGS to conventional risk factors in the prediction of CAD in primary care settings to improve the more efficient use of preventive interventions, such as lipid-lowering medications. Our investigation contributes to the increasing representation of individuals of diverse ancestry and varying socio-economic status in research studies, which we hope will help to decrease health disparities.13. All participants have consented for lifelong electronic health record access and genetic analysis. The study was approved by the London South East NRES Committee of the Health Research Authority (14/LO/1240). 97.4% of participants in G&H are in the lowest two quintiles of the Index of Multiple Deprivation in the UK. About two-thirds are British Bangladeshi and the remainder British Pakistani. The median age at recruitment was 37 (interquartile range [IQR] = 16) and 43 (IQR = 19) years for female and male participants, respectively  is a community-based cohort of British Pakistani and Bangladeshi individuals recruited primarily in East Londonely Fig. . The cohp < 1 × 10−6) in a subset of samples with low level of autozygosity (Supplementary Methods). We excluded individuals who did not have Bangladeshi or Pakistani ancestry , and those who self-reported as coming from other ethnic groups or who did not report this information  individuals had linkage to primary or secondary care data, of which 56.5% were female. Quality control of genotype data was performed using Illumina’s GenomeStudio and plink v1.9. We removed variants with low call rate (<0.99), rare variants with minor allele frequency (MAF) < 1%, and variants that failed the Hardy–Weinberg test , MAF < 0.5% .We applied more stringent QC on GWAS results using the EasyQC package followedN = 61,377) were downloaded from dbGaP (Accession number: phs001584.v1.p1). Quality control of genotype data and imputation to the Human Reference Consortium (HRC) reference panel have been described previously40. We projected eMERGE participants onto the PC space generated from the 1000 Genomes project phase 3 dataset and applied Uniform Manifold Approximation and Projection (UMAP)41, and identified 43,877 European ancestry individuals. Well-imputed (INFO ≥ 0.3) bi-allelic SNPs with MAF ≥ 0.1%  were retained for downstream analysis.We used European ancestry samples from the eMERGE cohort (henceforth eMERGE), a consortium of US medical research institutions, to carry out comparisons with G&H. Network Phase III data (42 (Supplementary Data Coronary artery disease (CAD) cases and controls in G&H were defined using the same ICD10 and OPCS4 codes as Khera et al. 36 in G&H using the R package “QRISK3” v0.3.043. We used data available up until 1 January 2010 to calculate QRISK3 (Supplementary Methods). Definitions of variables in the QRISK3 algorithm are shown in Supplementary Data 8.We calculated the QRISK3 10-year predicted risk for CADPhenotype data in eMERGE were downloaded from dbGaP . Data on medications affecting lipid and BP measurements were not available, so the highest measurements for LDL, TC, SBP, and DBP were used when comparing heritability estimates and performance of PGSs with G&H in order to minimise the effects of medications.44 and adjusted for age, age2, sex and the first twenty principal components. For total cholesterol and LDL-C, adjustments were made for use of statins as described above.GWAS was performed with SAIGE45. For CAD, we estimated SNP heritability on the liability scale using 6.7% as the prevalence estimate in the US46, and 3.33% for the UK background population from which G&H is sampled, defined as all people from South Asian ethnicities  registered with a primary health physician/GP in four east London boroughs.Datasets that were used in analyses are provided in Supplementary Data https://github.com/brielin/Popcorn) to estimate the trans-ancestry genetic correlations between G&H and UKBB European ancestry individuals while accounting for differences in LD structure (Supplementary Methods)47. A two-sided p-value < 0.05 indicated that the genetic correlation was significantly different from one.For the genetic correlation analyses, we used GWAS summary statistics generated in European ancestry individuals from UK Biobank (UKBB), since we needed a larger sample size of ancestrally homogeneous individuals than is available through eMERGE to obtain accurate estimates. We used Popcorn (23. We assessed whether published trait-associated loci were reproducible in G&H (Supplementary Data r2 ≥ 0.8) within a 50 kb window (based on the European ancestry 1000 Genomes data) of the sentinel variant and with p-value < 100 × psentinel. The locus was defined as being ‘transferable’ if at least one variant from the credible set was associated at two-sided p < 0.05 with the relevant trait in G&H, and the direction of effect matched in both datasets. For loci harbouring multiple signals, we only kept the most strongly associated variant . The statistical power to observe an association of a given locus in G&H was calculated using alpha = 0.05, the effect size estimate for the lead variant from the European ancestry discovery GWAS, and the allele frequency of the variant and sample size in G&H (Supplementary Methods). For SBP and DBP, the raw measurements were used in the discovery GWAS, we thus calculated power with effect size estimates in UK Biobank European-ancestry individuals by Neale’s group where normalised blood pressure values were used. The power estimates were summed up across published loci for a given trait to give an estimate of the number of loci expected to be significantly associated in G&H. This is the expected number if all loci are transferable and account for the statistical power for replication. We calculated the power-adjusted transferability (PAT) ratio by dividing the observed number of loci with p < 0.05 amongst the published loci in G&H over the expected number. To our knowledge, this is a novel approach for assessing the reproducibility of GWAS findings.Previous studies that evaluated the reproducibility of GWAS loci in South Asian individuals did not formally account for differences in power or LD patternsp < 0.05 and no variant within 50 kb of locus had p < 1 × 10−3 in G&H. LocusZoom (http://locuszoom.org/) was used to create regional association plots.We also highlighted published trait loci that we deemed to be ‘non-transferable’ despite sufficient statistical power: they contained at least one variant in the credible set with >80% power for replication and yet none of the variants in the credible set had https://github.com/KarolineKuchenbaecker/TEColoc)30 which tests whether a specific locus has the same causal variant in two groups with different ancestry, and applied it to G&H and UKBB European ancestry individuals (Supplementary Methods).We used the Trans-ancestry colocalisation method (TAColoc)  from the PGS Catalogp-value thresholding method (C + T) and optimised PGSs in G&H and eMERGE separately using 10-fold cross-validation (Supplementary Methods). Lastly, in G&H we calculated meta-PGSs proposed by Marquez-Luna et al. 18 and PGS using the PRS-CSx method19 that incorporated GWAS summary data from the panUKBB South Asian-ancestry individuals (Supplementary Methods).We also calculated PGSs using the clumping and 2, and the first 10 genetic PCs, and (2) the reference model which accounted for the covariates only. For continuous risk factors, linear regression was fitted, and the gain in R2 when adding PGS as an additional predictor, or incremental R2, was calculated as the difference between the R2 of the full model and the reference model. Logistic regression was used to assess the associations between PGSs and CAD. The area under the receiver operating characteristic curve (AUC) was estimated for both models with the R package “pROC” v1.16.2 and incremental AUC was calculated similarly. We performed bootstrap resampling of individuals 1000 times to estimate the 95% confidence intervals for incremental R2 and incremental AUC. We estimated the effect size per SD of PGS from the full model. Effect size for quintiles, and for the top 10% versus middle 40–60% was reported as well. Relative accuracy was calculated as the ratio of incremental AUC  in G&H to that in eMERGE.Age at recruitment was used as a covariate for analysis of disease status, and age at measurement for analysis of quantitative traits. PGSs were standardised to a mean of 0 and SD of 1. We fitted the following two models: (1) the full model which had PGS and covariates namely sex, age, age8 to integrate QRISK3 scores with the PGS for CAD developed by Wang et al. 15. Cox regression was performed using the R package “survival” v3.2-7. The concordance indices (C-indices) of the following models were compared: (1) age at assessment + sex, (2) PGS + age at assessment + sex, (3) QRISK3, and (4) the integrated score. We calculated the continuous net reclassification index (NRI) and categorical NRI  for the integrated score compared to QRISK3 alone. NRI was calculated as the sum of NRI for cases and NRI for controls (noncases):QRISK3 scores were calculated for 8112 unrelated individuals as described in Supplementary Methods (420 CAD cases and 7702 controls). We followed Riveros-Mckay et al. P(up|case) and P(down|case) indicate the proportions of cases that had higher or lower risk estimates using the integrated score, respectively. For categorical NRI, P(up|case) indicates the proportions of cases that were reclassified as high-risk individuals (i.e. with <10% risk by QRISK3 but >10% by the integrated scores). We calculated NRI in two age groups , as well as in age-by-sex subgroups. Bootstrap resampling (1000 times) was used to estimate confidence intervals for NRI. All reported p-values are two-sided.For continuous NRI, 49 framework using the cardiometabolic traits  as exposures. To identify genetic instruments for the exposure, we explored three alternative approaches: (a) published loci significant at p < 5 × 10−8 in the original European ancestry GWAS; (b) transferable loci defined as described in the transferability section of the methods, taking the effect size from the original European ancestry GWAS; and (c) loci significant at p < 5 × 10−8 in the South Asian ancestry group of the Pan-UKBB GWAS, LD-clumped to an r2 < 0.2 with a LD window of 50 kb, based on South Asian 1000 Genomes project LD reference. Where insufficient numbers of genome-wide significant instruments were identified, we used a more permissive p-value threshold of p < 5 × 10−5 for instrument selection in UKBB South Asian. The primary Mendelian randomisation analysis was performed using, as outcome, summary association data from the G&H CAD GWAS performed as described above, using the inverse-variance weighted method under a random effect model, implemented with the TwoSampleMR R package v0.5.550. For comparison, a two-sample Mendelian randomisation approach was also performed using summary data for CAD from eMERGE and established loci significant at p < 5 × 10−8 in the original European ancestry GWAS. We also undertook several sensitivity analyses. In brief, we evaluated the Egger intercept to assess directional pleiotropy and Cochran’s Q statistic51 as an indicator of heterogeneity. Mendelian randomisation analysis using MR pleiotropy residual sum and outliers methods (MR-PRESSO)52, weighted median53 and weighted mode methods54 models were additionally performed in the presence of heterogeneity. To investigate the individual direct effect of HDL-C, LDL-C, and TG on the risk of CAD and simultaneously account for horizontal pleiotropy, we replicated the analysis in a multivariable MR (MVMR)55 setting with the TwoSampleMR R package. Genetic instruments for the HDL-C, LDL-C, and TG joint exposure were selected from samples with European ancestry in UKBB, if associated with at least one of the three lipids (p < 5 × 10−8). The instruments were filtered based on MAF > 0.005 and LD-clumped to an r2 < 0.01 with a window of 50 kb, based on the EUR 1000 Genome project LD reference, using plink2. All reported p-values are two-sided.We modelled liability to CAD as our outcome within a univariable two-sample Mendelian randomisation (MR)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-15Reporting Summary


DNA mutations are the fundamental source of genomic variations that lead to phenotypic differences between individuals. Genomic variations in a population are usually assayed by single nucleotide polymorphism (SNP) arrays or whole-genome sequencing (WGS) to obtain genotype counts. If phenotypic measurements are also available on genotyped individuals in this population, genotype counts can be statistically linked to phenotypic measurements, i.e., genome-wide association studies (GWAS). Decades of GWAS in humans  and animUnlike genomics research in humans, GWAS in animals is usually carried out amongst related individuals with small effective population sizes. This results in many SNPs in high linkage disequilibrium (LD) from a locus being associated with a trait, and it is difficult to distinguish which ones are causal. This is particularly difficult when the GWAS used imputed sequence variants  where a The genomic information of domestic animals is used to improve animal breeding. In particular, genomic selection or genomic prediction (GP)  using geThe ‘Multi-layered Genome-wide Association and Prediction in Animals’ research topic intends to collect high-quality articles on the emerging area of integrating multi-omics datasets into GWAS and GP. In its conclusion, 9 articles from 58 authors have been collected, ranging from data generation, integrative analysis of multi-omics with GWAS and GP, and new method development across multiple domestic species.Shi et al.) that incorporate “global-local” shrinkage priors, and multi-class Bayesian Alphabet methods  that incorporate biological information into multi-trait Bayesian analysis. The application of these methods into simulated and real data supports that incorporating biological priors into GP training improves its accuracy.Developing new methods for the integrative analysis of multi-omics and GWAS/GP is one of the key research areas in genetics. Due to flexibility in incorporating priors in the model, several new Bayesian methods have been proposed, including BayesHP and BayesHE (Deng et al. show that imputation using low-pass WGS is more accurate than using SNP arrays. This was also found by Zhao et al. where real low-pass WGS from donkeys were generated, analyzed, and applied to GP.GWAS or GP using WGS is another emerging area. Due to high costs of in-depth WGS, there is a new shift toward using low-pass WGS which provides cost-effective options for GWAS or GP to use millions of sequence variants. By analyzing simulated data, Jiang et al. present such an effort in detecting loci associated with body size in Hu sheep. Also, Yang et al. identified loci associated with meat production in chicken. Apart from the standard linear mixed model, GWAS can also be carried out using single-step Bayesian regression, and Naserkheil et al. present such an effort in identifying loci associated with meat production traits of beef cattle.GWAS in animals has been largely used to dissect causative loci associated with complex traits. Gebreyesus et al. found that adding GWAS-prioritized variants had no improvement in GP for survival traits of dairy cattle which have very low heritability estimates. This emphasizes that more studies are needed in this area. Other lowly heritable but important traits in cattle included female fertility. Chen et al. found that accounting for sire genetic effects improves the genetic evaluation of fertility of Holstein cows.In fact, loci prioritized by GWAS may be used as biological priors to enhance GP. However, In conclusion, integrating multi-omics data with GWAS and GP in animals is an important and emerging research area in livestock genomics. We anticipate that the development and application of efficient methods, increased use of WGS, and integration of more types of multi-omics data will be future directions of this area. Understanding how DNA mutations shape complex traits not only furthers our understanding of biology, but also provides practical benefits in animal breeding.


QST vs. FST. We show that the macroscopic resultant trait (stem diameter), unlike its microscopic components (tracheid dimensions) and juvenile growth, was under divergent selection that predated the Last Glacial Maximum. Altogether, the current variation in these phenotypic traits in Norway spruce is better explained by local adaptation to ancestral environments than to current ones, where populations were partly preadapted, mainly through growth‐related traits.The current distribution and population structure of many species were, to a large extent, shaped by cycles of isolation in glacial refugia and subsequent population expansions. Isolation in and postglacial expansion through heterogeneous environments led to either neutral or adaptive divergence. Norway spruce is no exception, and its current distribution is the consequence of a constant interplay between evolutionary and demographic processes. We investigated population differentiation and adaptation of Norway spruce for juvenile growth, diameter of the stem, wood density, and tracheid traits at breast height. Data from 4461 phenotyped and genotyped Norway spruce from 396 half‐sib families in two progeny tests were used to test for divergent selection in the framework of  Those were often located in the Iberian, Italian, and Balkan peninsulas, but surviving populations could also be distributed on less discrete spots and, instead, scattered along the southern edge of the ice sheet Bennett, . ImproveNew abiotic and biotic pressures encountered during the glacial cycles presumably led to new adaptations. In many tree species, current phenotypes partly reflect past selection  H. Karst.) is a dominant boreal conifer species that thrives nowadays in a vast northern domain spanning from Norway to central Russia, as well as in smaller European domains, such as the Alps and the Carpathians  and quantitative trait (QST) genetic divergence offers a theoretically well‐grounded, accessible, and powerful tool for assessing the role of natural selection in such a population differentiation scenario , tracheid radial width , tracheid tangential width , tracheid wall thickness , diameter at breast height  calculated from ring widths, and the age when the trees reached breast height (ABH). Although ABH is related to upright growth, it can be estimated accurately with high‐quality increment cores from old trees. The measurements, identification of annual rings, and the importance of ABH are discussed in Lundqvist et al.  for 11 rings per offspring in most of the cases and their ABH.2.4Picea abies , we additionally filtered out SNPs with less than five individuals per genotype to avoid a leverage effect. We performed a loose filtering for variant call rate (or missingness) with PLINK v1.9  were normalized by their respective overall means  to allow comparison between traits. Then, they were analyzed at each cambial age separately, resulting in 6 traits over 11 rings, a total of 66 traits in addition to ABH.Yijkl” is the trait of the l‐th offspring of the k‐th family in the j‐th block at the i‐th site, “μ” is the grand mean, “si” is the fixed effect of the i‐th site, “Bij” and “Fk” are uncorrelated random effects of the j‐th block nested in i‐th site (with variance σB2) and the k‐th half‐sib family (with variance σF2), respectively, and “εijkl” is the residual fitted to a Gaussian distribution of variance σ2. Heritability was estimated as 4 × σF2/(σF2 + σB2 + σ2) as an intraclass correlation estimation of half‐sib families l” is the fixed interaction effect between ABH and the genetic cluster of the l‐th offspring, “(c × s)l: is the fixed interaction effect between ABH and the site of the l‐th offspring, and the other terms are as defined above in model (1). Model (2) was applied to the 66 traits, as previously mentioned. Since the interaction between ABH and the site effect was always significant, we split the dataset per site, as recommended by Crawley , and the other terms are as defined above in model (1). QST was estimated as σA2/(σA2 + 2σF2), and averaged across rings. Following the recommendation of Whitlock , and significance of a slope was assessed using a Student's t‐test (function t.test of the R package stats). To account for the unbalance caused by consecutive filtering, we only kept families with at least four offspring per site. Applying all the previous filters, we ended up with 396 half‐sib families and 4461 offspring per trait. Multiple testing across the 66 traits were accounted for by considering a Bonferroni adjusted cut‐off of p = 7.6 × 10−4.All computations were done using R v4.0.4 , except for the third ring TWT . Every trait of the southern populations was consistent with a higher investment in growth than in wood density .At all cambial ages, phenotypic variation of all six phenotypes  was associated with the geographical origin of the genetic cluster, as the genetic cluster effect was significant for all rings ). As mentioned earlier, ABH was latitudinally structured, half‐sib families of southern origin having low ABH reflecting higher early growth rates.By definition, ontogenic tempo is given by the ABH. It had a relatively higher environmental variance, and therefore a relatively lower heritability of 17.31% than other traits  seem to have been absorbed by the genetic cluster and the family effects, and let the feature “ABH” only reflect environmental factors out of a lesser correlation with the response variable: when correcting for multiple testing, in model (2), the effects of ABH and the genetic clusters were significant for more than 70% of the phenotypes (47 of 66 for both), whereas their interaction was significant for <2% of the phenotypes  up until the 9th ring , then a decrease until the 13th ring . This can also be seen in figure 5a of Lundqvist et al. : acclimation mostly impacted early (3rd and 4th) or later (9th and higher) rings, and least impacted intermediate rings (5th to 8th). More precisely, analysis of variance of model (2) showed that, for early rings, ABH significantly impacted every trait but one trait. For intermediate rings, ABH significantly impacted only 6 of 20 traits. For later rings, ABH significantly impacted every trait but four traits . On the opposite, fast growers had increasingly lower WD, as the effect of ABH increased from the 3rd ring to the 13th ring (from 4.93 × 10−3 to 3.1 × 10−2). Fast growers also had significantly larger 9th rings, as the effect of ABH on RW followed a convex curve matching its concave significance, reaching its largest negative value at the 9th ring (−5.82 × 10−1). At all cambial ages, the effect of ABH was smaller for tracheid properties  than for macro‐phenotypes   Figure . These r3.4t > 4.36, p < 0.002), apart from TRW . The significance of the genetic cluster in model (2) on the 13th ring was large for DBH and RW , intermediate for WD and TWT , and low for TRW and TTW . Presumably, the significance of TWT might be due to its high correlation with the significant WD (⍴ = 0.88). The variation in growth‐related macro‐phenotypes, namely DBH and RW, therefore reflected the population structure more pronouncedly than did tracheid dimensions or wood density , was only significant for DBH (p = 0.0115), RW (p = 0.0086), and marginally so for TTW (p = 0.0437). Despite the QST of TTW being marginally significant, what is striking is that TRW, which is a component of DBH and RW, is not (p = 0.2680). Comparing QST and FST demonstrate that selection, if any, would primarily act upon growth‐related macroscopic traits. This strong association between population structure and signatures of selection made impossible the inference of genetic variants underlying phenotypic variation  in spite of its clear relation with growth. Either this trait is neutral, which is highly unlikely as early‐stage selection is known to be strong (e.g., Saleh et al., The strongest genetic divergence was observed for growth‐related traits, and secondly for wood density, suggesting that most of the adaptation is associated with radial or upright growth. Divergent selection acting at early life stages most likely led to the accumulation of competitive advantages, causing then an observable ontogenetic shift of the effect of population structure. The average value of QST and FST is due to the covariance between effect sizes, and the covariance between allelic frequencies (reviewed in Le Corre & Kremer, The difference between 5As expected, phenotypic variation reflected the population structure of Norway spruce. The fact that the phenotypes were latitudinally ordered suggests that the origin of population was strongly confounded with adaptation. Teasing apart the demographic history and the adaptation history is challenging, more so that it has been demonstrated that population history had a strong impact on phenotypic divergence in Norway spruce (Lagercrantz & Ryman, The authors declare that there is no conflict of interest.Appendix S1Click here for additional data file.