Hordeum vulgare L.) thrives in the arid and semi-arid regions of the world; nevertheless, it suffers large grain yield losses due to drought stress. A panel of 426 lines of barley was evaluated in Egypt under deficit (DI) and full irrigation (FI) during the 2019 and 2020 growing seasons. Observations were recorded on the number of days to flowering (NDF), total chlorophyll content (CH), canopy temperature (CAN), grain filling duration (GFD), plant height (PH), and grain yield (Yield) under DI and FI. The lines were genotyped using the 9K Infinium iSelect single nucleotide polymorphisms (SNP) genotyping platform, which resulted in 6913 high-quality SNPs. In conjunction with the SNP markers, the phenotypic data were subjected to a genome-wide association scan (GWAS) using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). The GWAS results indicated that 36 SNPs were significantly associated with the studied traits under DI and FI. Furthermore, eight markers were significant and common across DI and FI water regimes, while 14 markers were uniquely associated with the studied traits under DI. Under DI and FI, three  and five  markers, respectively, had pleiotropic effect on at least two traits. Among the significant markers, 24 were annotated to known barley genes. Most of these genes were involved in plant responses to environmental stimuli such as drought. Overall, nine of the significant markers were previously reported, and 27 markers might be considered novel. Several markers identified in this study could enable the prediction of barley accessions with optimal agronomic performance under DI and FI.Barley ( Hordeum vulgare L.) is the fourth most important cereal crop worldwide [Triticum spp.) and corn  [Barley (orldwide . Barley orldwide . Barley orldwide . Barley mays L.) . Despitemays L.) , increasAdditionally, the gap between cereal grains production and consumption is increasing because of the ongoing climate change and population and income growth, particularly in arid and semi-arid regions ,7,8,9. FDrought stress tolerance is a complex quantitative trait controlled by many small-effect genes, and is often confounded by the growth stage . The repField evaluation for drought stress tolerance is expensive, time-consuming, and highly affected by growth conditions . EvaluatThe emergence and development of new DNA sequencing and high throughput genotyping technology has enabled the routine use of single nucleotide polymorphism (SNP) markers for marker-assisted selection (MAS) and gene pyramiding . The sinPrior knowledge of the association between a marker and a trait is used in MAS to select for that trait in contemporary plant materials . TherefoDespite the attractiveness of MAS, the progress made in molecular plant breeding for quantitative traits such as drought remains limited ,40. ThisIn the present study, 426 elite barley lines from several US barley breeding programs were planted and evaluated in Egyptian field conditions under full irrigation (FI) and deficit irrigation (DI) to identify potential SNP markers associated with important traits such as the number of days to flowering (NDF), total chlorophyll content (CH), canopy temperature (CAN), grain filling duration (GFD), plant height (PH), and grain yield (Yield). The elite lines used in the current study have not previously been evaluated for drought stress. The results presented herein provide useful information on the importance of testing location and phenotyping for drought stress under drought-prone environments. After validating the novel markers discovered in this study under several environments and using different plant materials, these markers can be converted into Kompetitive allele-specific PCR (KASP) markers. KASP markers can be used in marker-assisted breeding schemes to accurately select allele donor parents or pyramid multiple alleles in the same genotype to enhance drought tolerance in barley. Therefore, novel markers associated with the traits identified in the current study are valuable for barley breeders in Egypt and other similar geographic regions.https://triticeaetoolbox.org/barley/, accessed on 2 October 2021) and in our previous publication [A panel of 426 spring barley lines and four commercially grown local check cultivars were investigated in this study. The barley lines were imported into Egypt from the barley breeding program at the University of Minnesota, MI, USA, in 2012. Further details regarding the lines’ description, pedigree, and the original development programs can be found on the T3/barley website  were collected directly before planting and analyzed according to Klute et al. . The mai2O5/ha in the form of superphosphate (15.5% P2O5). Potassium fertilizer was applied at 125 kg K2O/ha in the form of potassium sulfate  in two equal doses. The first dose was applied during seedbed preparation, while the second was applied after 30 days from sowing. Nitrogen fertilizer was used at the rate of 144 Kg N/ha in the form of ammonium nitrate (33.5%) in three doses; the first dose was applied at sowing, and the other two equal applications were at 21 and 45 days after sowing. During the booting growth stage, the plots were sprayed with TILT250 , which has Propiconazole as an active ingredient with a volume of 0.25% v.v., followed by Punch 40% EC  at 0.185% v.v. after ten days from the TILT treatment to suppress disease development. For the first and second growing seasons, the sowing dates were 12 November 2018 and 16 November 2019, respectively. Chemical and manual weed control management were conducted to provide weed-free conditions across all trials.During seedbed preparation, several steps were followed to minimize soil heterogeneity, specifically, disking to mix the previous crop’s residues with the sub-soil and laser leveling of the soil surface for improved distribution uniformity of the irrigation water. The recommended agronomic practices were applied in both growing seasons. Phosphorous fertilizer was applied during seedbed preparation at a rate of 75 Kg PTrials were subjected to drought stress by controlling irrigation, in which all genotypes were grown under 100%  and 50%  of the crop water requirements. All trials across growing seasons received the first irrigation directly after sowing to ensure uniform germination. Under FI, subsequent irrigations were applied after depleting 50% of the available water capacity (AWC). The AWC of the soil was calculated by subtracting water content at a permanent wilting point from water content at field capacity. When needed (50% of AWC), plots under FI were re-irrigated to 100% of the AWC in which the soil moisture content reached the field capacity. Irrigation intervals (days) for FI treatments were calculated by dividing the AWC (mm) by crop water requirements (mm/day). However, plants grown under DI received 50% of their water requirements by increasing the number of days between irrigations to double (compared to FI), resulting in a soil moisture content of ~30–35% of the AWC before re-irrigation. A water flow meter was installed in each trial to estimate the volume of the supplied water. The AWC and water requirements of barley were estimated according to Morsy et al. , in whicThe number of days to flowering (NDF) was recorded visually as the number of days from sowing to anther exertion of 50% of the initial spikes. The total leaf chlorophyll content (CH) was estimated during the flowering stage using a spad-502 chlorophyll meter  on three randomly selected plants from each plot. The average of these three plants was used in the statistical analysis. Readings of CAN were measured during the flowering stage using a hand-held infrared thermometer . The CAN readings were taken from the same side of each plot at a one-meter distance from the edge and approximately 50 cm above the canopy at an angle of 30° to the horizontal. Readings were performed during the mid-day (between 1:00 and 3:00 pm) on sunny days. An average of five instantaneous CAN readings were recorded from each plot and used afterward in the analysis. The grain-filling duration  was measured as the period from flowering to physiological maturity. Plant height  was measured after physiological maturity on a random sample of five plants in each plot as the distance from the soil surface to the tip of the spike, excluding awns. Grain yield  was measured by cutting all plants in each plot. After three days of air drying, plants were threshed using a locally made single plot thresher.https://triticeaetoolbox.org/barley/downloads/downloads.php (accessed on 2 October 2021).The lines used in the current study were genotyped using the 9K Infinium iSelect SNP genotyping array  from theThe two water regimes (full and deficit irrigation) were assigned to the main plots, with two replicates within each trial. Genotypes were assigned to the subplots and arranged randomly into ten incomplete blocks of size 43. The size of the experimental unit (plot size) was four rows wide by 1.5 m long with 20 cm between rows within each replicate and growing season.A single environment analysis was conducted for the four trials (two years combined with two water regimes). Within each trial, analysis of variance, the approximated broad-sense heritability (sometimes known as repeatability ) and theH) within trials was estimated as follows: Moreover, the approximate broad-sense heritability  incorporating the two- and three-way interactions between growing seasons, genotypes, and water regimes for all phenotypic traits were implemented with the growing season, irrigation, genotypes, and genotypes × growing season, genotypes × irrigation, and genotypes × irrigation × growing season were tested as fixed effects. Moreover, the incomplete blocks nested within the complete blocks, growing season, and irrigation were treated as a random effect and used as an error term to test the significance of the growing seasons, irrigation, and irrigation × growing season . The besH) across environments was estimated as follows:Approximate broad sense heritability  = 5. Estimaing the marker effect from the BLINK method was not applicable; thus, the markers effects were estimated by fitting the mixed effect model while simultanusely including both Q and K matrices.The estimated BLUEs for the phenotypic measurements obtained from each trial in conjunction with the SNP markers were subjected to a genome-wide association scan (GWAS). The GWAS scan was conducted using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) implemented in GAPIT version 3 . The firhttps://www.uniprot.org/ [Oryza sativa L.) and Arabidopsis  Heynh.) genomes and their corresponding protein information.The significant markers detected from the GWAS scan were annotated using the IBSC_v2 barley genome assembly developed by the international barley sequencing consortium . The flarot.org/ . FurtherThe mean performance, coefficient of variance, and broad sense heritability for all traits obtained with deficit (DI) and full irrigation (FI) for the two growing seasons, i.e., 2019 and 2020, were calculated in order to investigate the quality and suitability of the collected phenotypic data for genome-wide association analysis GWAS, .2) for NDF under DI was 93 and 88% for the first and second growing seasons, respectively. Moreover, H2 for NDF under FI was 89% during the first and second growing seasons.During the first growing season (2019), the NDF means were 86.8 and 90.92 days under deficit (DI) and full irrigation (FI), respectively. During the second growing season (2020), the NDF means were 83.30 and 87.32 days under DI and FI, respectively. The largest coefficient of variance  for NDF was obtained from the FI regime in 2019, while the lowest CV (0.78%) was obtained from the FI in 2020. The broad-sense heritability (H2) for CAN under DI was 85 and 80% for the first and second growing seasons, respectively. Moreover, H2 for CAN under FI was 77 and 83% for the first and second growing seasons, respectively.The mean CAN during (2019) was 31.57 and 26.32 °C for DI and FI, respectively. The mean CAN for the second growing season (2020) was 31.57 and 26.32 °C under DI and FI, respectively . The lar2) for CH under DI was 77% for the first and second growing seasons. Moreover, H2 for CH under FI was 87 and 84% for the first and second growing seasons, respectively.During the 2019 and 2020 growing seasons, CH the means under DI were 31.57 and 29.22 SPAD units, respectively, while under FI, CH was 26.32 and 24.30 SPAD in 2019 and 2020, respectively. The CV for CH under DI was 5.96 and 8.55% for the first and second growing seasons, respectively. Moreover, the CV values for the FI were 5.28 and 8.92% in 2019 and 2020, respectively. The broad-sense heritability (H2) for GFD under DI was 92 and 86% for the first and second growing seasons, respectively. Moreover, H2 for GFD under FI was 90 and 88% for the first and second growing seasons, respectively.The mean GFD during (2019) was 26.27 for DI and 30.84 days for FI. In the second growing season (2020), the mean GFD was 26.34 under DI and 31.35 days under FI. The largest CV (5.43%) for GFD was obtained from DI in 2020, while the smallest CV (4.68%) was obtained from DI in 2019. The broad-sense heritability , the mean yields were 3.12 and 3.85 tons/hectare under DI and FI, respectively. The largest CV (6.86%) for yield was obtained from DI during 2019, while the smallest CV (5.76%) was obtained from DI during 2020. The Hp < 0.01) for the Years (Y), Stress (DI and FI), and Genotypes (G) across all traits. Moreover, the combined ANOVA indicated a significant effect for the two- and three-way interactions across all traits. Among the four environments (two years and two water regimes), the highest heritability (94%) was observed for plant height, followed by GFD, Yield, and CH, which recorded heritabilities of 78, 73, and 72%, respectively. Furthermore, a moderate heritability (58%) was observed for NDF, while the smallest heritability (20%) was observed for CAN. The analysis of variance for the total chlorophyll content, canopy temperature, grain filling duration, plant height, grain yield, and the number of days to flowering is presented in The combined analysis of variance indicated a substantial effect for genotypes by water stress interactions across the six traits. Thus, the estimated BLUEs for each trait obtained from DI (2019 and 2020) and FI (2019 and 2020) in conjunction with the SNP data were subjected to a genome-wide association scan (GWAS).p-value of 3.22 × 10−5 (−log10 of 4.51), which is below the specified significance threshold. Moreover, all the significant SNP markers for DI and FI positively affected the NDF  of these nine markers was significantly associated with CAN under DI. Furthermore, under FI, the associated markers were distributed among chromosomes 1H , 2H (11_20099 and 11_10326), 4H (11_20020), 7H (11_10174) and one marker (12_31414) was not mapped to a chromosomal location. . All marThe GWAS results for the total Chlorophyll Content (CH) indicated that four markers were associated with CH under DI . One of Two markers were significantly associated with GFD under DI . These tUnder DI, five markers were significantly associated with PH . These fThe GWAS results for grain yield under DI indicated that eight markers were associated with grain yield . The eigThe GWAS results revealed that eight SNP markers are significantly associated with at least two phenotypic traits , suggestAdditionally, 11_10326 was found to be significantly associated with NDF under DI; CAN, GFD, and PH under FI and yield under both DI and FI. The third marker on chromosome 2H with pleiotropic effect is 11_21416, which was found to be associated with NDF under DI and FI, CH under DI, and PH under FI. One marker on chromosome 4H was significantly associated with NDF under DI and CH under FI. On chromosome 7H, two markers (11_20042 and 11_20170) with pleiotropic effect under DI were identified; 11_20042 was found to be significantly associated with NDF and CH, while 11_20170 was significantly associated with NDF and CAN.In the current study, 426 barley genotypes were evaluated under two water regimes DI and FI. Our results indicated a significant interaction between genotypes and water regime, implying a different pattern of response among the genotypes under DI compared to FI. Because the interaction between genotypes and water regime was significant, a separate GWAS scan was conducted for the studied traits under DI and FI in order to identify potential SNP markers uniquely associated with the studied traits under each water regime.The genotypes and SNP markers used in the current study were previously investigated to identify QTLs associated with phenotypic performance under normal growth conditions in the USA . FurtherHORVU7Hr1G121870.1 and HORVU3Hr1G089580, respectively. The gene HORVU7Hr1G121870.1 was found to play a role in repressing flowering and promoting defense gene expression and synthesis of defensive secondary metabolites in both barley and A. thaliana [HORVU3Hr1G089580 encodes Ribonuclease II. Ribonuclease is involved in the flowering and vegetative stages of development in other plants [One of the essential characteristics behind barley’s tolerance to drought stress is NDF. Genotypes associated with early flowering allow plants to complete pollination and grain development in a shorter period . Thus, ethaliana . The genr plants . Howeverr plants .The annotation of the marker 11_20042 indicated that it is involved in encoding the protein histone deacetylase 11. Histone deacetylase is involved in plant responses to abiotic stress . The thip < 1 × 10−5). In a previous publication, the marker 11_20099 was discovered to be associated with polyphenol oxidase activity and environmental adaptation in barley [One marker (11_20099) was significantly associated with NDF under both FI and under DI . Catalase 2 (CAT2) activity increases in leaves experiencing water deficits and H2O2 accumulation [Canopy temperature (CAN) is another vital trait that is essential in drought stress tolerance . Under Dmulation .HORVU4Hr1G058560 gene, which encodes plant-specific domain TIGR01589 family protein.The SNP marker 11_20020, which was found to be associated with CAN, was previously reported by ; they inHORVU1Hr1G094480, which encodes protein endopeptidase Clp. The protein endopeptidase Clp plays an essential role in chloroplast and protein degradation in senescing leaves [Marker 11_10326 was associated with CAN under FI and NDF under DI, and was previously reported to be associated with shoot dry weight and beta-glucan . Anotherg leaves . As partHORVU1Hr1G086110 gene, which encodes S-like ribonuclease. Marker 12_30298 was associated with CAN under FI; the annotation of this marker did not return any known barley genes. Marker 12_31414 was associated with CAN in this study and was annotated to the HORVU4Hr1G014490 gene.Marker 11_20908 was associated with CAN under FI and was annotated to the HORVU7Hr1G098440 gene, which encodes a cell wall-modifying enzyme rapidly upregulated in response to environmental stimuli such as drought [It is widely known that drought stress reduced the total chlorophyll content (CH) of the flag leaves . In tota drought . Marker HORVU2Hr1G023590 gene, which encodes B-S glucosidase 44. Marker 11_20384 was previously reported and found to be associated with NDF under DI. Marker 12_10910 was annotated to the HORVU6Hr1G021170 gene and was previously reported by Igartua et al. [HORVU7Hr1G105460 gene and was previously reported by Muñoz-Amatriaín et al. [Six markers  were associated with CH under FI. Markers 11_10563 and 11_20605 have no available annotation information. Marker 11_11061 was annotated to the a et al.  to be asHORVU7Hr1G021840 gene, which encodes cysteine synthase protein. The cysteine synthase protein is involved in drought tolerance in plants [Grain-Filling Duration (GFD) is another trait found to be adversely affected by drought stress, with drought causing a reduction in GFD . Under Dn plants .HORVU3Hr1G096210 gene, which encodes secondary cell wall-related glycosyltransferase family 47 protein. Marker 12_30793 was not annotated to known barley genes.Under FI, three markers  were associated with GFD. Marker 11_10326 was reported in this study before and found to be associated with CAN. Marker 11_11127 was annotated to the HORVU5Hr1G038720 gene, which encodes Eukaryotic translation initiation factor 2B (eIF-2B) family protein. Marker 11_20099 was found to be associated with CAN under FI and NDF under FI and DI. Marker 11_20790 was annotated to the HORVU3Hr1G034290 gene and was previously reported by Borràs-Gelonch and Romagosa [Drought stress reduces plant height (PH) by reducing cell-division enlargement and differentiation . Five maRomagosa , who fouHORVU3Hr1G111600 gene, which encodes a protein involved in plant immunity (ILITYHIA); this gene has a pleiotropic effect on plant size, with mutant plants tending to be smaller in size [Under FI, ten markers were associated with PH; three of these ten markers  were significantly associated with PH under DI. Moreover, among the significant markers for PH under FI, four markers  were previously reported and found to be significantly associated with other traits in this study. Only two markers (11_11411 and 12_30827) were uniquely associated with PH under FI. Marker 11_11411 was annotated to the  in size .HORVU4Hr1G010700.1, which encodes protein 50S ribosomal L12-2, a chloroplast precursor. Marker 11_10326 has unknown annotation information; however, an association between this marker and shoot dry weight and beta-glucan in barley was previously reported [HORVU3Hr1G096830 gene, which encodes serine carboxypeptidase-like protein (SCPL). SCPL plays a vital role in stress response, growth, development, and pathogen defense [HORVU6Hr1G053760 gene, which encodes a drought-responsive microRNA (FUS3-complementing gene2). That microRNA was previously identified in Sorghum bicolor (L.) and found to be responsive to drought [HORVU1Hr1G013680; this marker was reported in other studies, in which it was found to be significantly associated with grain yield [HORVU4Hr1G067520 gene, which encodes glycosyl hydrolases family 31 protein (GH31). GH31 is a diverse group with a range of different Carbohydrate-Active EnZymes (CAZy). In response to drought stress, GHs catalyse the hydrolysis of O- or S- glycosidic bonds to release sugars and provide the plant with energy for development.There have been several reports on the negative impact of drought stress on barley grain yield (Yield) production . Under Dreported . Marker  defense . Marker in yield . Marker HORVU5Hr1G124350, which encodes the DANA2 protein. Marker 12_31210 was previously reported and found to be associated with seed dormancy in barley [HORVU3Hr1G002800, which encodes DEA(D/H)-box RNA helicase family protein. This protein family is involved in abiotic stress tolerance in plants [Marker 12_31210 was annotated to the gene n barley . Marker n plants .Additionally, three markers  were associated with yield under FI. Marker 11_10326 was significantly associated with yield under DI, while 11_20099 was significantly associated with other traits in this study . Marker 11_20017 was uniquely significant with respect to yield under FI. This marker was not annotated to a specific barley gene. However, it was reported by other researchers and found to be associated with yield in barley .The plant’s overall performance under drought stress is influenced by the relationships among several morphological and physiological traits . In the Additionally, under FI, five markers  had pleiotropic effects on at least two traits. Interestingly, marker 11_20099 was significantly associated with NDF, CAN, PH, and yield, while 11_10326 was significantly associated with NDF, GFD, PH, and Yield. Moreover, marker 11_20840 was significantly associated with CAN, PH, and Yield, while 12_30298 was significantly associated with CAN and PH. Finally, marker 11_20605 was significantly associated with CH and PH. The pleiotropic effect of several of the significant markers detected in this study under DI and FI was expected, as the traits that we used are among the traits that play a key role in barley grain yield in both drought-stressed and favorable conditions. It is known that grain yield is the cumulative effect of many traits and that drought stress tends to reduce, NDF, CH, GFD, PH, and Yield while increasing CAN ,82.The interrelationships among NDF, CAN, CH, GFD, PH, and grain yield might explain the pleiotropic role of certain loci or markers in these traits. Several researchers have extensively studied the contributory relationship between NDF and grain yield ,83. TheyThis study identified 36 SNP markers with significant association with NDF, CAN, CH, GFD, PH, and grain yield. Furthermore, eight markers were significant and common across DI and FI regimes, while 14 markers were uniquely associated with the studied traits under DI. Moreover, under DI and FI, three  and five  markers, respectively, had pleiotropic effects on at least two traits. Among the significant 36 SNP markers, 24 were annotated to known barley genes. Most of these genes were involved in plant responses to environmental stimuli such as drought. Overall, nine of the significant markers were previously reported, and 27 markers might be considered novel. Several markers identified in this study could enable the identification of barley accessions with optimal agronomic performance under DI and FI. Further research is required to verify the novelty of the significant markers across multiple environments.


Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries. Cross-ancestry genetic correlation can reveal differences in the genetic basis of traits between populations, but methods can be biased by ancestry-specific genetic architecture. Here, the authors present a method to for estimating cross-ancestry genetic correlations, accounting for ancestry-specific genetic architecture. In humans, causal loci and their effects on complex traits are not uniformly distributed across populations such that the same trait can be genetically heterogenous between two ancestry groups5. Studies of cross-ancestry genetic correlation are therefore important to understand the genetic relationship between two ancestry groups when investigating a complex trait3. Understanding genetic correlation between two ancestry groups can inform us on how well we can predict a complex disease in one ancestry group based on the information on another.Complex traits are polygenic and influenced by environmental factors6. This paradigm-shift approach to estimate cross-ancestry genetic correlations have greatly increased the potential to dissect the latent genetic relationship between ancestry groups for complex traits.Genome wide association studies (GWAS) have been successful in identifying variants associated with causal loci, and can also provide a useful resource to investigate cross-ancestry genetic correlations. Using GWAS datasets, genomic relationships between samples across multiple ancestry groups can be constructed based on genome-wide single nucleotide polymorphisms (SNPs), which can be fitted with phenotypes across ancestries in a statistical model9 although Europeans represent only 16% of the global population12. Because of large samples, the estimated SNP associations in Europeans are far more accurate than those in other ancestries. As a matter of fact, the performance of polygenic risk prediction depends on the accuracy of estimated SNP associations, minor allele frequencies (MAF), linkage disequilibrium (LD)14, and environmental heterogeneity15, causing ancestral disparity in genetic prediction accuracy7. Therefore, cross-ancestry genetic studies are urgently required to bridge the disparity, e.g., estimated cross-ancestry genetic correlations may be able to inform if SNP effects estimated in European ancestry samples can be useful to predict the phenotypes of samples in other ancestry groups.Most GWAS have focused on European ancestry samples (>80%)18. For example, Yang et al.6 estimated the cross-ancestry genetic correlation between East Asians and Europeans for ADHD, where ancestry-specific allele frequencies were used to standardise samples’ genotype coefficients in estimating their genomic relationships. This method of cross-ancestry genomic relationships has been widely used for cross-ancestry genetic studies19. However, the method cannot account for the trait genetic architecture specific to each ancestry group, which is a function of the relationship between the genetic variance and allele frequency (one important aspect of a heritability model21). With an incorrect heritability model, estimated genetic variances within and covariance between ancestry groups are biased21, and hence the cross-ancestry genetic correlations cannot be correctly estimated. Another method based on GWAS summary statistics has been introduced (Popcorn)2. However, this method also cannot correctly account for diverse genetic architectures across ancestry groups.To date, several studies have been undertaken to estimate cross-ancestry genetic correlations between diverse ancestry groups for a range of complex traitsHere, we develop a novel method that can properly account for ancestry-specific genetic architecture and ancestry-specific allele frequency in estimating a genomic relationship matrix (GRM). In addition, we revisit the existing theory to correct mean bias in genomic relationships. In simulations, the SNP-based heritability and cross-ancestry genetic correlation estimated from our proposed method are shown to be unbiased, whereas other existing methods can generate biased estimates. We apply the proposed method to six anthropometric and five other complex traits from the UK Biobank data, e.g., standing height, body mass index (BMI), waist-hip ratio, basal metabolic rate, body fat percentage, pulse rate and education. For each trait, we estimate SNP-based heritabilities and cross-ancestry genetic correlations across five groups, i.e., White British, Other Europeans, South Asian and African ancestries, and a mixed ancestry group.22. In each ancestry group, we assume that the relationship between genetic variance and allele frequencies (heritability model) varies, i.e. the genetic variance at the ith genetic variant . Note that with α = 0, Eq.  spectrum, a heritability model with α = −0.5 has been widely used27. However, Speed et al.23 reported a different α value, e.g., α = −0.125 for anthropometric traits, using multiple European cohorts. While it is intuitive that α values can be varied across ancestry groups, it has not been well studied. Accounting for α correctly, we can disentangle Our main aim is to unbiasedly estimate cross-ancestry genetic correlation for a complex trait, using common SNPs  presented for both populations. We used publicly available data from the UK Biobank. Participants of the UK Biobank were stratified into multiple ancestries  according to their underlying genetic ancestry based on a principal component analysis  of various heritability models with different α values for the 6 anthropometric traits from the UK Biobank (see Methods). Second, we simulate phenotypes based on the UK Biobank genotypic data to assess if SNP-based heritability and cross-ancestry genetic correlation are unbiasedly estimated. In the simulation, various α values are used to generate allelic effects of SNPs in various ancestry groups, and the correlation of SNP effects between ancestry groups varies between 0 and 1 (see Methods). For simulated phenotypes of multiple ancestry groups, we estimate SNP-based heritability and cross-ancestry genetic correlation, using bivariate GREML28 with four existing methods to construct GRM as shown in Table 30. In addition to the existing methods, we use a novel method to estimate GRM in the cross-ancestry analysis in which we use ancestry-specific α value and ancestry-specific allele frequency, so that the estimation model matches with the ancestry-specific genetic architecture (see Methods). The equation for the proposed method can be written asith and jth individuals at the lth SNP, lth SNP  estimated in the two ancestry groups, ith and jth individuals belongs, and lth SNP in the two ancestry groups, dk_i is the expectation of the diagonals, lth SNP, 26 (see Methods and Supplementary Table xl), instead of its expectation , which is analogue to the approach of Speed et al. (2017)21. In terms of LD weights, we contrasted two kinds of heritability models, i.e. GCTA-α vs. LDAK-thin-α model. GCTA-α model has no LD weights, whereas LDAK-thin-α model explicitly considers LD among SNPs.We compared the Akaike information criteria (AIC) values of heritability models with varying α model, we observed that AIC values with α = −0.25, −0.125, −0.625, −0.75 and −0.825 were lowest for White British, Other European, South Asian, African, and mixed ancestry cohorts, respectively . In this simulation, we do not consider associations between the allelic effects and LD structure for any SNP, i.e., LDAK simulation model, because LDAK model was not particularly plausible for the genetic architecture of the traits, especially for White British, Other European and South Asian ancestry cohorts  instead of using a constant α value across ancestries (Methods). For these simulated phenotypes, estimated cross-ancestry genetic correlations using four existing methods were biased when the true genetic correlation was 0.5 or higher between White British and African ancestry cohorts or between South Asian and African ancestry cohorts. Biased estimates were still observed even when ancestry-specific allele frequencies were considered in GRM3 and 4 , Other European ancestry cohorts (p = 2.30e-05) and South Asian ancestry cohorts (p = 1.14e-03). The estimates for BMI and waist and hip circumference were generally high in African ancestry cohorts and low in Other European ancestry cohorts  between ancestry groups for 6 anthropometric traits are shown in Fig. p = 1.22e-02) and between African and Other European ancestry cohorts (p = 7.63e-04) were significantly different from 1 .Estimated cross-ancestry genetic correlations , between Other European and South Asian ancestry cohorts (p = 1.35e-02) and between African and mixed ancestry cohorts  , and between White British and African ancestry cohorts (p = 2.93e-01). White British and Other European ancestry cohorts were observed to be genetically homogeneous for the trait (p = 5.78e-01).For height, we observed a significant genetic heterogeneity between South Asian and African ancestry cohorts (03) Fig. . AlthougEstimated cross-ancestry genetic correlations for waist circumference and hip circumference showed a similar pattern with BMI, i.e., there was significant evidence for genetic heterogeneity between White British and African ancestry cohorts and between Other Europeans and African ancestry cohorts Fig.  and 20. We did not observe any significant heterogeneity across ancestries (genetic correlation estimate was not significantly different from 1) for waist-hip ratio Fig. . Non-estp = 6.83e-03), indicating a significant genetic heterogeneity between these two ancestry groups  for educational attainment . In the case of body fat percentage, there is no significant genetic heterogeneity among ancestries although estimated cross-ancestry genetic correlations between African vs. White British, South Asian vs. White British, and South Asian vs. Other European are lower than 1  for African vs. White British, p = 1.13e-08) for African vs. South Asian, p = 1.35e-05) for White British vs. South Asian, p = 4.64e-05) for Other European vs. South Asian and p = 5.01e-05) for Other European vs. African ancestries , the findings were changed and could be over-interpreted, e.g., there was additionally significant genetic heterogeneity between African and European ancestry cohorts .Cross-ancestry genetic correlations can provide crucial information in cross-ancestry GWAS and cross-ancestry polygenic risk score prediction. We showed a significant genetic heterogeneity for obesity traits  between African and European ancestry samples. For height, there was a significant genetic heterogeneity between African and South Asian ancestry samples. However, without considering ancestry-specific 29. Similar to LDSC, GWAS summary statistics-based cross-ancestry analyses (Popcorn2 and XPASS32) has been used by several studies18, to estimate cross-ancestry genetic correlations. However, as shown in the results from Brown et al.2 and our simulation, the methods can be biased when the genetic architecture of a complex traits is diverse  across ancestries. S-LDXR38 may be able to account for MAF-dependent genetic architecture, which, however, requires an arbitrary stratification of SNPs into multiple MAF bins that was not considered in this study. It is also reported that Popcorn, MAMA and S-LDXR are likely to be suitable for a pair of European and East Asian ancestries, but they have not been explicitly tested when other ancestries are involved, e.g., African or South Asian ancestry.LDSC based on GWAS summary statistics is computationally efficient and has been widely used in a single ancestry group (dominantly used in Europeans) in the estimation of SNP-based heritability and genetic correlations between complex traitsα model only in the real data analysis, assuming that all SNPs contributed equally to the heritability estimation39. We did not use the LDAK-thin-α model that required to prune SNPs within each ancestry group, which could substantially reduce the number of common SNPs between two ancestry groups in cross-ancestry genetic correlation analyses. Secondly, we did not consider MAF stratified, LDMS, or baseline model42 in estimating cross-ancestry genetic correlations because it was developed to estimate SNP-based heritability, but not suitably designed to estimate genetic correlations. Furthermore, it is required to fit multiple random effects , which is not computationally efficient. Nevertheless, these advanced models can improve the estimations of ancestry-specific SNP-based heritability and cross-ancestry genetic correlation. Thirdly, we estimated optimal scale factors (α) with a moderate sample size especially for South Asian or African ancestry cohorts, resulting in a relatively flat curve of ΔAIC values. A further study is required to estimate more reliable α for South Asian or African ancestry cohorts with a larger sample size. For educational attainment, non-genetic factors, such as socio-economic status and access to public education, might also contribute to the phenotypes. In this study, we did not consider how the genetic effects interact with such environmental factors, i.e., genotype-by-environment interaction, which may partly underlie the significant genetic heterogeneity across ancestries. A further study is required to elucidate how genotype-by-environment interaction cause cross-ancestry genetic heterogeneity, especially for educational attainment. Finally, when the causal SNPs may not be 100% common between ancestries, the estimated cross-ancestry genetic correlations should be carefully interpreted with caution although the estimates of the proposed method can be less biased, compared to existing methods.There are several limitations in this study. Firstly, we used the GCTA-In conclusion, we present a method to construct a GRM that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. As the result, our method can provide unbiased estimates of ancestry-specific SNP-based heritability and cross-ancestry genetic correlation. By applying our proposed method to anthropometric as well as other complex traits, we found that obesity is a genetically heterogenous trait for African and European ancestry cohorts, while human height is a genetically heterogenous trait between African and South Asian ancestry cohorts. For educational attainment, there is significant genetic heterogeneity between African and European, African and south Asian, and south Asian and European ancestries.yi is the vector of phenotypic observations, bi is the vector of fixed  effect with the incidence matrix Xi, gi is the vector of random additive genetic effects with the incidence matrix Zi and ei is the vector of residual effects for the ith ancestry group (i = 1 and 2).We use a bivariate linear mixed model (LMM) to estimate SNP-based heritability and cross-ancestry genetic correlation, using GWAS data comprising multiple ancestry groups. In the model, a vector of phenotypic observations for each ancestry group can be decomposed into fixed effects, random genetic effects and residuals. The LMM can be written asgi and ei, are assumed to be normally distributed, i.e. gi ~ N .The random effects, ith causal variant and M is the number of causal variant. The genetic variance at the ith genetic variant can be written asith variant if we do not consider the relationship between 24. When considering the relationship between 23. This shows that, although α. Therefore, as shown in Eq. .This shows α values vary across populations23. Therefore, although per-allele effect size , optimal 25; Yang et al.26) of25) ofAij is the genomic relationship between the ith and jth individuals, L is the total number of SNPs, lth SNP, and ith individual at the lth SNP.GRM is a kernel matrix and can be normalised with a popular form . The generalised form isα is a scale factor, d is the expected diagonals, α = −0.5, Eq.  c c\documefbias, should bed is redefined as Therefore, a revised formula, considering een Eqs.  and 8)  fbias, sx). Unless the samples are completely homogenous, the expectation is an approximation of var(x). So, var(x) should be used instead of the expectation 2p(1-p). Therefore, the formula can be rewritten asFurthermore, 6 proposed a GRM method that uses ancestry-specific allele frequency to be applied to cross-ancestry genetic analyses, which can be expressed aslth SNP estimated in the ith and jth individuals belongs. When estimating cross-ancestry genetic correlation for Attention Deficit Hyperactivity Disorder (ADHD) between European and East Asian (Han Chinese), Yang et al.6 considered the standard scale factor for both ancestry groups (α = −0.5 in White European and East Asian). Similarly, Guo et al.3 also used Eq. .Yang et al.α value can be changed across ancestry groups because of genetic drift, natural selection, and various selection pressures. Combined with a revised formula derived above , a noved as Eq. ,\documenα across ancestries, which can provide unbiased estimates of per allele effect size on the original scale in each ancestry, i.e. correctly estimating kth and lth ancestry groups, not This proposed method allows us to account for heterogenous mentclass2pt{minimhttps://www.ukbiobank.ac.uk/)44. The genotypic data were imputed based on Haplotype Reference Consortium reference panel45. The UK Biobank data comprised 488,377 participants and 92,693,895 SNPs. The participants were grouped into four ancestry groups  and one mixed ancestry cohort, according to their genetic ancestry estimated from a principal component analysis based on the genome-wide SNP infromation22. Mixed ancestry cohort includes individuals from South Asian ancestry, some of white and black African and white and black Caribbean ancestries and those individuals assigned as other ancestry groups in UK biobank  > 0.01, Hardy–Weinberg equilibrium p > 10−04. We also excluded individuals outside 50. In the analysis, we retained HapMap3 SNPs only as these are high in quality and well calibrated to dissect genetic architecture of complex traits52.We performed additional stringent QC in each of the ancestry groups. The QC criteria include an INFO score (an imputation reliability) ≥0.6The initial sample size was 430,301, 29,023, 7449, 7647 and 16,615 for White British, Other European, South Asian, African, and mixed ancestry cohorts, respectively. After quality control, the cleaned data includes 288,837, 26,457, 6199, 6179 and 11,797 participants, and the total number of SNP was 1,154,490, 1,148,504, 939,512, 729,534 and 513,362 for White British, Other European, South Asian, African, and mixed ancestry cohorts. For White British ancestry cohort, we randomly selected 30,000 from the QCed 288,837 individuals for estimating cross-ancestry genetic correlations because it is computationally feasible in multiple analyses paring with other ancestry groups.ith SNPs were scaled by actual variance α and The phenotypes of each ancestry group were simulated using a bivariate linear mixed model  for all the UK Biobank individuals55.We analysed six different anthropometric traits  from the UK Biobank across different ancestries . These traits were adjusted for demographic variables, UK biobank assessment centre, genotype measurement batch and population structure measured by the first 10 principal components (PCs)α and LDAK-thin-α models23 were used to determine optimal α values for each of the ancestry groups. We considered various α values, e.g. α = −1, −0.875, −0.75, −0.675, −0.5, −0.375, −0.25, −0.125, 0 and 0.125, following the approach of Speed et al21. All GRMs with various α values were estimated using LDAK software20, which set an equal weight to all SNPs in GCTA-α model and different weights to SNPs according to their LD scores in LDAK-thin-α model. Using GRMs, SNP-based heritabilities were estimated for six anthropometric traits, using a multivariate linear mixed model53 that fit six anthropometric traits simultaneously. Note that in the multivariate model, we treated the six traits independent without considering residual and genetic correlations between traits. This was because the optimal alpha value should be trait-specific as our main analysis was to estimate trait-specific cross-ancestry genetic correlation, i.e., equal weights from all six traits to obtain the optimal α value for each ancestry group. Finally, we identified optimal α that gives lowest AIC values, i.e., GCTA-57 analyses were used to estimate heritability and cross-ancestry genetic correlation. In the analyses, we used four existing GRM methods (Table 50 (-make-grm-gz for command GRM1 and GRM2) and GCTA27 software (-sub-popu command for GRM3 and GRM4). The distribution of diagonal elements and off-diagonal elements across ancestries represented in Supplementary Figs. α = −0.5 and estimated in PLINK2). We also used Popcorn16, which could be based on GWAS summary statistics. For these methods, we calculated empirical SE and its 95% confidence interval (CI) over 500 replicates to assess the unbiased estimation of the methods for each combination of ancestry pairs.Bivariate GREMLds Table  to assesFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Here, we re-sequenced the nucleotide sequences of the maize ZmFWL7 gene, a member of the FWL family, in 256 elite maize inbred lines, and the associations of nucleotide polymorphisms in this locus with 11 ear-related traits were further detected. A total of 175 variants, including 159 SNPs and 16 InDels, were identified in the ZmFWL7 locus. Although the promoter and downstream regions showed higher nucleotide polymorphism, the coding region also possessed 61 SNPs and 6 InDels. Eleven polymorphic sites in the ZmFWL7 locus were found to be significantly associated with eight ear-related traits. Among them, two nonsynonymous SNPs (SNP2370 and SNP2898) showed significant association with hundred kernel weight (HKW), and contributed to 7.11% and 8.62% of the phenotypic variations, respectively. In addition, the SNP2898 was associated with kernel width (KW), and contributed to 7.57% of the phenotypic variations. Notably, the elite allele T of SNP2370 was absent in teosintes and landraces, while its frequency in inbred lines was increased to 12.89%. By contrast, the frequency of the elite allele A of SNP2898 was 3.12% in teosintes, and it was raised to 12.68% and 19.92% in landraces and inbred lines, respectively. Neutral tests show that this locus wasn’t artificially chosen during the process of domestication and genetic improvement. Our results revealed that the elite allelic variants in ZmFWL7 might possess potential for the genetic improvement of maize ear-related traits.Plant  Zea mays L.) is a major feed, food, and fuel (ethanol) crop in many parts of the world. Although global maize production has exceeded 1.2 billion tonnes in 2021/2022 (Statista 2021), the supply is still insufficient to meet the escalating demand. Breeders’ main goal has always been to increase yield. Grain yield is primarily determined by the morphological traits of the kernel and ear. Kernel yield is a complex quantitative trait that is influenced by various ear-related traits, including ear length (EL), ear weight (EW), ear grain weight (EGW), ear row number (ERN), kernel number per row (KNR), kernel size, and kernel weight (Maize (l weight . It is kl weight . Among tl weight . Kernel l weight . TherefoFruit-weight 2.2 (fw2.2), initially isolated from the tomato (Solanum lycopersicum L.), is a major gene that regulates fruit size and yield (FWL genes encode proteins that contain a conserved cysteine-rich PLAC8 domain (CCXXXXCPC or CLXXXXCP), and are associated with various phenotypic traits, including organ size, plant architecture, and fruit weight  genes. Fw2.2 and its FWL/CNR homologs can not only regulate organ size and regulate cell division, but also endow plants with a response to rhizobia inoculation  from 256 inbred lines, 71 landraces, and 32 teosintes. We examined the relationship between this natural variation in this gene and several phenotypic features, including kernel yield and size. This study was designed to: 1) identify natural variations in ZmFWL7 connected to ear-related traits; 2) determine favourable alleles which are beneficial for HKW within ZmFWL7; and 3) assess the significance of ZmFWL7 in maize domestication and yield improvement.t weight , whereasng vigor . AlthougIn total, 256 elite inbred lines, 71 landraces, and 32 teosintes were collected in our study . All theZmFWL7 locus were re-sequenced using targeted sequence capture technology on the NimbleGen platform by BGI (Beijing Genomics Institute) Life Tech Co., China. The genomic sequences and positions of the ZmFWL7 (GRMZM2G173742) gene in the inbred line B73 (Fresh leaves of 256 elite inbred lines were used to extract their genomic DNA using the modified cetyl trimethyl ammonium bromide (CTAB) method. Subsequently, the genomic sequences of the maize line B73  were useline B73 . The oriZmFWL7 locus was performed using ClustalX software. The alignments were further manually edited to modify the putatively false alignment pairs (π) of the ZmFWL7 locus was estimated as the mean number of nucleotide differences per site between sequences using the R package PopGenome. A sliding window method was used with a window size of 200 base pairs (bp) and a step length of 50 bp  was estimated between any pairs of polymorphic sites in the sequenced region of ZmFWL7. The LD heatmap and r2 were generated using the R packages LDheatmap and pegas, respectively  variations within the ZmFWL7 locus are summarised in ZmFWL7, a total of 175 variants, including 159 SNPs and 16 InDels, were discovered. SNPs and InDels were found every 21.7 and 251.6 kb on average. The overall nucleotide diversity (π) of the ZmFWL7 locus was 0.0087 for all 256 inbred lines; however, the coding region exhibited a much lower frequency of nucleotide polymorphisms compared with the downstream region. In addition, a sliding window of 200 bp with a step length of 50 bp was used to calculate π and θ  between any two phenotypic traits were determined. Interestingly, most traits were statistically significantly correlated, with EW/EGW exhibiting the greatest correlation (r = 0.98). Only 8 of the 55 pairwise correlation tests for ear-related traits did not reach a significant level  . Therefore, the study of kernel length, width, and thickness should be conducted in the context of kernel weight.Eleven ear-related traits were determined, and the descriptive statistics are presented in d KL/KT) . Notablyp < 0.01, p-value was less than 0.001. SNP2370  and SNP2898 (lysine to glutamine) were significantly associated with HKW, respectively, which explains the 7.11% and 8.62% phenotypic variation  ≥ 0.01. A marker-trait association study was carried out using mixed linear models (PCA + Kinship) . The quaariation . MoreoveLinkage disequilibrium (LD) analysis indicated that SNP2369, SNP2370, SNP2657, and SNP2871 showed a relatively high linkage with SNP2898 . Among tDwarf8 , but its frequency trend increased and was 4 times higher in landraces and more than 6 times higher in inbred lines. In addition, the allele T of SNP2370 was completely absent in teosintes and landraces, but the frequency of this allele was increased to 12.89% in inbred lines. These findings indicated that these excellent allele variations have the potential values in maize breeding.Although the selection strategies for crop genes vary at different selection stages (domestication or improvement), it is undeniable that many genes that control important agronomic traits have been subject to crop domestication and subsequent selective breeding . The fungulation . In thisZmFWL7 need to be further investigated, our findings indicate that SNP2370 and SNP2898 may be used for marker-assisted selection to improve maize breeding.In summary, it is possible to increase kernel yield by using this gene in the maize population. Although the functional characteristics of


Specifically, we used a genomic best linear unbiased prediction (G-BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado “criollo” “plus trees.” Estimated additive genetic parameters were validated at a 5-year-old common garden trial , in which 22 OP half-sib seedlings from 82 elite “plus trees” served as rootstocks for the cv. Hass clone. Heritability (h2) scores in the “criollo” “plus trees” ranged from 0.28 to 0.51. The highest h2 values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2–0.8) and 0.51 (CI 0.2–0.8), respectively. The h2 scores for the agronomic traits ranged from 0.34 (CI 0.2–0.6) to 0.39 (CI 0.2–0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits’ weight, respectively, exhibited h2 scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite “criollo” “plus trees” may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from “plus trees” via OP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite ‘plus trees’, and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability— Grafting is an ancient technique used for vegetative propagation, especially in perennial fruit crops. This method, used on woody and herbaceous plants, can improve several agronomic characteristics, such as yield and vigor, as well as tolerance to biotic and abiotic stresses . In fruiFrom a physiological perspective, such confounding factors are pervasive, too. Broadly speaking, grafting affects three main processes at the tree level: uptake and transport of water and nutrients , productPersea americana Mill.) cv. Hass  half-sib interracial seedling rootstocks derived from selected “criollo” “plus trees” . TherefoPhytophthora cinnamomi Rans. Yield and adaptive traits are then left as second and third steps within avocado rootstock breeding. To guarantee that selected rootstocks exhibit a stable phenotypic effect, avocado nurseries in temperate latitudes often rely on clonal rootstock propagation  identify promissory avocado “criollo” “plus trees” as seed donors for rootstock production based on their genetic-estimated breeding values for heritable morphological and agronomic traits. We hypothesize that heritable variation for key traits would enable half-sib seedling rootstock selection from elite “criollo” “plus trees.” Specifically, we have relied on a total of 104 avocado “criollo” trees between 40 and 50 years old, identified as “plus trees” in the province of Antioquia (northwest Colombia). These avocado trees are long-term survivors across different remote agro-ecological zones, so we expect that they may serve as potential donors of seedling rootstocks due to their presumably outstanding adaptability and trait values . These “criollo” avocados had previously been characterized with microsatellite  markers to study their genetic diversity, as well as in terms of their morpho-agronomic traits  estimate additive genetic parameters  across promissory avocado “criollo” “plus trees,” (2) quantify the inheritance of agronomic traits of avocado “criollo” “plus trees” in cv. Hass c traits ,b. Therein situ characterized by taking morphological data, and carrying out their molecular fingerprinting (section below on “microsatellite markers characterization”). Collection of yield data was additionally carried out in these trees during four consecutive years (2014–2017).A total of 104 avocado “criollo” “plus trees” were selected in different remote agroecological zones up to 2,400 m asl of the province of Antioquia, northwest Andes of Colombia, during the years 2014 and 2015 , as well as in their OP half-sib seedling progenies used as rootstocks for the Hass scion (2017–2021).The seeds produced by selected elite avocado “criollo” trees were germinated at greenhouses, and obtained 3-month-old seedlings were grafted with cv. Hass scions. In the year 2015, the cv. Hass grafts were planted at a common garden  at AGROSAVIA’s research station La Selva, located in Rionegro, province of Antioquia, at 2,100 m asl. Morphological data, such as length and diameter of the rootstock, the canopy length and diameter of the scion, and the morphology of the graft scar were collected during the years 2017–2021. The production data of the cv. Hass grafts were collected in the same period from 2017 to 2021. Since half-sib seedling rootstocks grafted with the Hass clone have been in production during five consecutive years in the same environment , we were able to minimize environmental variance and equal phenotypic variance to the rootstocks’ additive genetic component, which is the main benefit of carrying out common garden or provenance tests. These trials intend to neutralize environmental effects over phenotypic expression, by planting diverse genotypes in a single locality . Our setcor.test and heatmap functions in R v.3.4.4 (R Core Team). Complete phenotypic measures were possible for a total of 82 “criollo” “plus trees” from the original sampling.A total of 17 morphological and agronomic traits were measured during the sampling trips of 2014 and 2015 in the avocado “criollo” selected “plus trees,” according to descriptors that are specific for avocado based on the International Plant Genetic Resources Institute (IPGRI). These descriptors included tree, leaf, fruit, and seed traits . Fruit pFurthermore, a total of 22 OP half-sib seedling progenies of 82 elite avocado “criollo” “plus trees” were used as rootstocks for the Hass scion. Grafted trees were characterized at AGROSAVIA’s La Selva research station in terms of their early growth traits , as well as in terms of their production  5 years after grafting (2017–2021). The analyses focused on yield traits because they allowed for a direct comparison with the recorded production traits in the “criollo” “plus trees,” besides the fact that yield traits are of paramount interest for commercial avocado cv. Hass orchards, especially those aiming for exportation markets.Total genomic DNA was obtained from avocado “criollo” leaves based on the extraction method standardized by ® ADN polymerase . Forward primers were labeled with WellRed fluorescent dyes on the 5′ end . The reactions were carried out in an I-cycler  thermo cycler using the following temperature profile for multiplex PCR 2 and 3: an initial step of 1 min at 94°C, 35 cycles of 30 s at 94°C, 30 s at 50°C, and 1 min at 72°C, and a final step of 5 min at 72°C. For multiplex PCR 1, the same temperature profiles were used, except for 55°C for annealing temperature that was used instead. The PCR products were analyzed by capillary electrophoresis in the equipment ABI PRISM® 3130 Genetic Analyzer . Allele sizes were estimated in base pairs with a Peak Scanner , allowing for a maximum of two alleles per sample.Three multiplex PCR amplifications were performed in 10 μl volume containing 16 mM (NH4)2SO4, 67 mM Tris-HCl pH 8.8, 0.01% Tween20, 2 mM MgCl2, 0.1 mM each dNTP,0.4 μM of each primer, 25 ng genomic DNA and GoTaqG-BLUP (genomic best linear unbiased predictor) model to quantify genetic parameters  for key agronomic traits across the avocado “criollo” “plus trees” selected as seed donors for the OP half-sib progenies used as rootstocks for the Hass clone. Genetic parameters are a baseline of any breeding program  a distance tree by means of the Neighbor Joining algorithm  enabled the use of the G-BLUP predictor. Hence, we relied on this G relationship matrix to compute the genetic-based G-BLUP model, following The admixed origin of the avocado “criollo” genepool, due to avocado’s protogynous dichogamy, is known to favor an adequately variable Genebank . The vary corresponds to the phenotypic trait vector, a is the vector of individual random additive genetic effects with a normal distribution [in other words, a ∼ N , where G is the among-tree pedigree-free SSR-based relationship matrix, and a2σ corresponds to the additive genetic variance , e is the vector of residual effects, and X and Z are the incidence matrices for fixed effects and additive genetic effects  implemented in the BGLR  software (Parameter estimation for the software  under anh2), which is the proportion of the overall phenotypic variance accounted for additive genetic variance, was then calculated by retrieving the additive (aσ2) and residual (eσ2) variances from the RKHS algorithm, following Narrow sense rootstock-mediated heritability  was computed per trait using Pearson’s correlation between observed and predicted (μ + GEBV) trait values .The vector of t values . Predicth2 scores for key agronomic traits in the OP half-sib seedling progenies of avocado “criollo” “plus trees” grafted with the Hass scion. This heritability analysis included 5 years of data in the OP half-sib seedling progenies of avocado “criollo” “plus trees” used as rootstocks for the Hass scion via the mother vs. half-sib regression analysis  were significant for the 11 measured morphological traits and for the 6 agronomic traits, according to the 95% CIs from the posterior distribution, all of which were above absolute zero  for the 11 morphological traits ranged from 0.28 to 0.51. The tree shape and branching traits presented the h2 lowest scores, 0.28 and 0.29, respectively. The highest h2 values were presented for ribbed petiole and adaxial veins with 0.47 and 0.51, respectively. The 6 agronomic traits evaluated presented very similar h2 values that ranged from 0.34 to 0.39. Seed weight was the trait with the lowest h2 score (0.34), while fruit weight and total volume traits exhibited the highest values, both with 0.39 (The estimated heritabilities (ith 0.39 .r = 0.64) pulp weight , adaxial veins , and ribbed petiole . Meanwhile, the trait that presented the narrowest confidence interval was the shape of the tree with 0.1–0.4 and r = 0.69  pointed toward “criollo” trees ANSS9 and ANNE55. Similarly, the trait vectors for total fruits’ volume, fruits’ length, and fruits’ weight, respectively pointed toward the avocado “criollo” “plus trees” encoded as ANDA62, ANCI75, and ANCH65. Meanwhile, the avocado “criollo” tree encoded as ANTU57 was closed to the vectors for the traits fruit diameter and pulp weight, which also makes ANTU57 a promising donor for seedling rootstock families . On the h2 scores for key agronomic traits, such as total number of fruits and fruits’ weight after 5 years of production. The estimation of overall h2 scores (spanning both the mother-offspring and the rootstock-mediated inheritance) for both agronomical traits followed a mothers vs. grafted half-sib offspring linear regression. In this sense, the dependent variable corresponded to the average half-sib family trait values for total number of fruits and fruits’ weight measured in avocado cv. Hass trees grafted on top of OP seedling rootstock families obtained from the avocado “criollo” “plus trees.” The trait values for the total number of fruits and fruits’ weight measured in the original mother “criollo” “plus trees” were depicted in the X-axis . The trait fruits’ weight exhibited a slope value of 0.056 with an r score of 0.18, and therefore an overall h2 estimate of 0.11 , and their OP half-sib seedling families. The SSR markers that were used for the molecular characterization of avocado “criollo” “plus trees” allowed obtaining G-BLUP heritability predictions of morphological and agronomic traits. After all, highly polymorphic SSR markers screened across variable genotypes , enable BLUP predictions, as demonstrated by via G-BLUP work either on the basis of shared relatedness  or on the basis of genetic hitchhiking due to linkage disequilibrium (LD) among marker loci. In the absence of demonstrated LD , shared relatedness as inferred by variable SSR markers across a diverse panel  and their OP half-sib seedling progenies used as rootstocks for the Hass scion (0.11), exhibiting a reduction of almost half in heritability. This indicates that nearly 50% of “plus tree” genetic gain for this trait may be inherited to cv. Hass through seed-mediated grafting.We estimated heritability values for 17 morpho-agronomic traits in avocado “criollo” “plus trees” selected as seed donors for rootstocks of the cv. Hass. These values were significant according to their 95% confidence intervals and heritabilities were found to be in a medium range according to h2T) of potential genetic gain for a key yield trait as is the total number of fruits. This overall effect totalizes the additive genetic contribution from OP half-sib seedling rootstock families to the Hass clonal scion . Ultimately, these estimates reinforce the feasibility of utilizing selected avocado “criollo” “plus trees” as seed donors for rootstocks.A long-standing debate in the breeding of fruit tree crops is whether seedling rootstocks are capable to enhance productivity in the absence of more uniform clonal rootstocks, which in turn are thought to lack sufficient genetic variability for long-term adaptation. As an intermediate solution, replacing seedlings genotypes by a diverse panel of elite clones is expected to convey major improvements while controlling adaptive genetic erosion . Nonethein situ superior “criollo” “plus trees” as donors of seedling rootstocks. Alternatively, buds from these selected “criollo” “plus trees” may be grafted to establish ex situ seed orchards. In any case, avocado cv. Hass nurseries may still face uncertainty while sorting the OP half-sib offspring from those “criollo” “plus trees” families in the absence of age-age correlations at the seedling stage. To assist early within-family selection of superior half-sib progenies, heritability estimates above 35%  also invite implementing last-generation high-throughput genomic selection (GS) schemes , a major fungal threat for avocado plantation worldwide  seedling screening at nurseries of avocado cv. Hass, as well as within the avocado rootstock breeding cycles per se. Nonetheless, a question persists, which is whether the studied SSR molecular markers are enough to capture sufficient additive variance as to make predictive breeding reliable. This potentially remains open judging by the r scores (> 0.54) of our pedigree-free SSR-inferred estimation of genetic parameters , as well as from these half-sib rootstock families to the avocado cv. Hass clonal scion . These future studies should also envision collecting further data across years . Altogether, these efforts will permit strengthening our modeling effort. Such baseline knowledge will further allow implementing predictive breeding platforms to guide before-grafting seedling selection at nurseries, while targeting diverse climates , such as number of fruits with exportation quality, as well as those discarded because of low weight or thrips” damage. The feasibility to harness “criollo” seedling diversity within a rootstock-breeding program reinforces the utility of native avocado trees to source cryptic genetic variation and adaptive potential (Our work has innovated the implementation of a pedigree-free marker-inferred heritability prediction model to assess the potential of avocado “criollo” “plus trees” as seed donors to produce OP half-sib rootstock families for the Hass clonal scion. The otential , ultimatotential  of clonaThe raw data supporting the conclusions of this article will be made available by the authors upon request, without undue reservation.GC-G, AN-A, and AC conceived the original sampling and research questions. GC-G and SS-O performed phenotypic data collection and sampling of leaves for genotyping. GC-G carried out DNA extractions, SSR genotyping, and alleles size estimation. GC-G, FL-H, and AC filtered input datasets and carried out data analyses. FL-H and AC plotted diagrams. GC-G, FL-H, AN-A, and AC interpreted heritability scores. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Significant genetic correlations of the change rate with the subject-specific mean were observed for FEV1/FVC (1/FVC (1/FVC showed significant heritability of SNP-by-smoking interaction (1 (rs4793538), FEV1/FVC , and post-bronchodilator FEV1/FVC (rs2445936). We found statistically significant evidence of heritability role on the change in lung function, and this was shared with the effects on cross-sectional measurements. We also found some evidence of interaction with smoking for the change of lung function.Genetic influence on lung functions has been identified in previous studies; however, the relative longitudinal effects of genetic factors and their interactions with smoking on lung function remain unclear. Here, we identified the longitudinal effects of genetic variants on lung function by determining single nucleotide polymorphism (SNP) heritability and genetic correlations, and by analyzing interactions with smoking. Subject-specific means and annual change rates were calculated for eight spirometric measures obtained from 6622 Korean adults aged 40–69 years every two years for 14 years, and their heritabilities were estimated separately. Statistically significant ( ChronicFEV1/FVC ,7,8. In ely 0.37 .1, FVC, and FEV1/FVC [KANSL1, HLA-DQ, NPNT, TET2, TSEN54, and HHIP, in non-smokers, and RBM19-TBX5 in heavy smokers [Genome-wide association studies have identified specific SNPs associated with FEVFEV1/FVC . Several smokers ,14,15,16 smokers .Epidemiologic and genetic investigations of pulmonary function require longitudinal observation of lung function trajectories. Although a few studies followed subjects for more than 10 years ,18, to tIn the present study, we utilised data from the Korean Genome and Epidemiology Study (KoGES) cohort . All ind1, and post-FEV1/FVC was small; these test results were available only from subjects in Ansung. The average profile plots of the eight lung functions at each period showed a decreasing trend for all lung functions (Study subjects were obtained from the Ansan (urban community) and Ansung  cohorts, which were followed up biennially. Each subject underwent a maximum of eight measurements. The numbers of observed measurements of the eight lung functions at each period are shown in unctions b,c.The statistical analyses required estimates of the mean and annual change rates for each subject, for which we included 6622 participants (3569 Ansan and 3053 Ansung) who were followed up ≥3 times . Since s1, across the eight periods, was 2.7. Both of the estimated mean values of these two traits were in the range of average normal values, even though both were slightly lower. The mean of FEV1/FVC ratio was 77.97%, which is also within the normal range above the cutoff of 70% by the GOLD criteria [To estimate the importance of genetic determinants for the eight lung function traits, we calculated the subject-specific mean (criteria .p = 1.16 × 10−5) followed by post-bronchodilator FEV1/FVC . 1/FVC exhibited the highest p = 0.0099) followed by FEV1/FVC , we calculated the correlation . 1/FVC; 1/FVC), which indicates that subject-specific means and annual change rates may be affected by different environmental factors. We also checked other traits but did not find any significant genetic correlations.For lung function traits with significant 1/FVC and post-bronchodilator FEV1/FVC. However, except for the post-bronchodilator FEV1/FVC of the never-smoker group, Subjects were separated into ever- and never-smokers and subgroup analyses were performed. stimated . The est1/FVC showed significant estimation in 1/FVC was significant at the 0.05 significance level (p = 0.02). The other traits are listed in We also evaluated the heritability of the SNP–smoking interaction  of FEVn CASC17 ,27,28, F [FAM13A ,26,29, P3A [PID1 , and TNXD1 [TNXB  have beeIn this study, we used two different heritability analyses of subject-specific means and annual change rates to estimate heritability for eight lung function traits. Using a 14-year follow-up of Korean population-based cohort dataset, we estimated the genetic correlations between the mean and annual change rate; subgroup and interaction analyses were also performed according to smoking status. To the best of our knowledge, this is the first study to report the heritability of the decline in lung function and other measurements in a population-based setting and Asian ancestry.1/FVC, and post-bronchodilator FEV1/FVC showed significant results. By comparing the estimated heritability via subgroup analyses between never- and ever-smokers, we found that the heritability of subject-specific means was higher in the never-smoker group. In the interaction analysis, we observed that the annual change in post-bronchodilator FEV1/FVC, FEV1/FVC, and MVV showed a significant or near-significant SNP–smoking interaction, thus allowing us to infer the amount of genetic variance that would be affected by smoking.In our analysis, we found that the heritability of subject-specific means was significant for all eight lung function traits. The heritability of change in lung function, FEV1/FVC), similar to previous studies on heritability decline [1/FVC exhibited the most substantial estimated heritability for annual change rate among the eight traits; FEV1/FVC also displayed significant heritability. Moreover, the heritability of these traits was more than half of the subject-specific means, which suggested that genetic effects played an essential role in the annual change of these lung function traits.Previous cross-sectional estimates of pedigree-based heritability, compared with SNP-based heritability, have shown that lung function traits range from approximately 20 to 40% ,17,32. I1 and FEV1/FVC patterns differ in subjects, with four different possible trajectories obtained using 29-year follow-up data [Generally, the lung function of healthy subjects peaked in early adulthood, followed by a steady decline. Subjects who failed to reach the predicted level of peak lung function at an early age had a higher chance of being affected by COPD . Recentl-up data . These o-up data ,36. It w-up data . Thus, l1/FVC and post-bronchodilator FEV1/FVC of ever-smokers was slightly, albeit significantly (at the 0.05 significance level) higher than that of never-smokers, with differences of 7% and 9%, respectively. Conversely, the heritability estimates of annual change rates did not reach significance, potentially owing to insufficient sample size. We also performed SNP–smoking interaction analysis, and annual change rates of post-bronchodilator FEV1/FVC, FEV1/FVC, and MVV showed significant and near-significant results, respectively, suggesting that the genetic variance for these three traits was affected by smoking in the middle-aged population.Environmental factors could influence the estimation of lung function heritability. Traditionally, smoking is considered the most crucial factor to influence the decline in lung function . However1/FVC and post-bronchodilator FEV1/FVC. As these metrics consist of genetic and environmental components, the positive correlations between their SNP effects indicate that subjects with higher genetic risk for subject-specific means of traits, such as FEV1/FVC and post-bronchodilator FEV1/FVC, tend to have a higher genetic risk for annual change rates as well. If the genetic effect for subject-specific means is fixed, the conditional variance of genetic effect on annual change rates , with annual change rates for FEVectories ,40. Howe−8, are shown in 1 was the most significant among the eight traits. This variant is located near CASC17, and interestingly, located in the upstream of SOX9, which was reported to be associated with FEV1 in previous studies [SOX9 was shown to be upregulated in adenocarcinoma of the lung, and its gene expression was associated with cell proliferation and lung development [FAM13A, which was found to be associated with FEV1/FVC ratio and COPD susceptibility in the prior GWAS [FAM13A is much higher in the lung tissue of COPD cases compared to controls, and the increased gene expression levels are associated with the risk allele [SPHKAP and PID1, are associated with FEV1/FVC for the first time. In the prior investigations, PID1 was associated with FEV1/FVC ratio [PID1 has a role in tissue homeostasis and cell growth [PID1 induces mitochondrial dysfunction in adipocytes [PID1 overexpression may contribute to COPD development via mitochondrial malfunction and excess reactive oxygen species [TNXB, was also previously reported to be associated with lung function traits (FEV1 and FEV1/FVC) and COPD [We also performed genome-wide association analyses using subject-specific means and annual change rates of the lung function as the response, where sex, average age, and height were adjusted as covariates. Because of the small sample size, GWAS with the smoking subgroup were not conducted, and it is discussed in our future study as well as a validation study. The GWAS results for all samples, with a genome-wide significance level of 5 × 10 studies ,41. SOX9elopment . For theior GWAS ,43,44,45VC ratio  and was l growth , and thaipocytes . Further species . Anotherand COPD ,50.CEP164 was associated with post-bronchodilator FEV1/FVC. CEP164 was previously reported to be critical for ciliogenesis during differentiation of airway multiciliated cells implicated in respiratory function [1/FVC and the locus in CEP164 in our analysis provides the evidence that the cilia in the airway play an important role in lung function decline.In the annual change rates analysis, we identified that SNP rs2445936 near function . In COPDfunction ,53. TherOne of the limitations of this study was sample size. Although the sample size was sufficiently large to support analysis of the complete data, it was limited for subgroup analysis. This led to significant standard errors for heritability estimation in the analysis. Previous studies have also reported that SNP heritability estimates are affected by the sample size when using GCTA . Thus, tIn summary, we estimated SNP heritability for eight lung function traits using a two-stage method to estimate cross-sectional averages and annual change rates in a Korean population-based longitudinal dataset. Our findings will help to understand aetiology of lung function decline and may facilitate the discovery of genetic factors influencing lung function-related traits. A larger sample size and novel statistical approaches will be required in future studies to validate and extend our findings.The KoGES cohort  consists1, FEV1/FVC ratio, the average forced expiratory flow during the mid (25–75%) portion of the FVC (FEF 25–75%), and maximal voluntary ventilation (MVV). To investigate the exact COPD prevalence as well as bronchodilator response in a sub-population, we also performed a post-bronchodilator test for FVC, FEV1, and FEV1/FVC ratio, after short-acting bronchodilator treatment. These traits were measured by technicians using a portable spirometer  according to standardised protocols of the American Thoracic Society [1 values were within 0.15 L [We considered pre-bronchodilator absolute values, which included FVC, FEV Society . All parn 0.15 L . Addition 0.15 L ,56,57.i at time point j was ijy, and ijy was a subject-specific function of the subject’s age, ageij. For our data, j was a number from 1 to 8. The subject-specific function was if and We assumed that the trait of subject Based on this definition, we estimated the subject-specific means  and annual change rates for the trait; two different SNP-based heritability parameters, both see .If s (GCTA) . The avek and time point j was taken as jk,w and the weighted linear regression was performed for each subject i using the following equation:A two-stage method was used to calculate g et al. . First, i for trait k at Here, ed model , and theed model . This moTo conduct subgroup analyses, we separated subjects into ever- and never-smoker groups. The ever-smoking group included past and current smokers. The heritability of subject-specific means and annual change rates were estimated using GCTA for the two groups.To estimate the variance in the SNP–smoking interaction for both subject-specific means and annual change rates, we applied the genotype-environment (GxE) interaction model, in which the main effects of environmental factors were included as fixed effects, while GxE interaction effects were treated as random effects (using the “--gxe” option of GCTA).Genome-wide association studies were conducted for eight lung function traits. Quality control and imputation were conducted, and the detailed procedure is described in  ONETOOL  and PLIN ONETOOL .