The rate of genetic gain for grain yield was estimated at 8.75 kg·ha−1 year−1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha−1 year−1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha−1 cycle−1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential. We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield. In an effort to identify and characterize the elite breeding pool of IRRI’s irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012–2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·haThe online version contains supplementary material available at 10.1186/s12284-021-00533-5. To this end, we gathered historical data from 102 yield trials in the IRRI phenotypic database . Despite the smaller sample size for the earlier historical periods, an upward trend from 1960 to 1980 is apparent followed by a period of variability in the average breeding value which eventually plateaus around 4.38 t·ha−1 after 2008. In order to interrogate the drivers of this genetic trend further, the equivalent complete generation  to assess the evolution of the crossing strategy and its relationship to EqG. During this period, 13,190 crosses were made. The number of crosses and the proportion of the type of cross varied substantially from one year to the next  was calculated to be 22, indicating a reasonable genetic diversity considering the census population size of 72. Cluster analysis based on genetic distance among ECP lines revealed two main clusters, which further branched into six sub clusters  and also boasted similar EqG measurements (averaging 5.26 to 6.33) with no significant difference between clusters .In order to quantify the genetic variation available to breeding in the ECP, the panel was genotyped with an amplicon panel of genome-wide markers specifically chosen to be informative among elite indica lines known as the 1k-RiCA see “” sectioners Fig. B. These Pita—74%, Pi25—100% and Pid2—100%), moderate to low for five genes . For the remaining genes  the favorable allele was absent from the ECP. For BLB, Xa4 (100%) and Xa26 (75%) presented high allele frequency but with some uncertainty due to missing data. Xa5 and sweet13 were also evidenced in the ECP with frequencies of 26.9% and 31.8%, respectively. Xa21 and Xa7 were also present but very low frequencies (below 3%). Xa13, sweet14, Xa23 were absent in the ECP. Concerning BPH, only BPH17 (15%) and BPH3 (65.5%) were found in the ECP. Finally, favorable alleles for STV11, TBV1, TSV1 and Chit1 were also present in the ECP.In addition to genome-wide genetic diversity, we also assessed the allele frequency of 33 high value genes contributing to the resistance to major biotic stresses in rice , brown plant hopper (BPH), gall midge (GM), rice stripe virus (RSV), rice tungro virus (RTV), rice yellow mottle virus (RYMV), and sheath rot (SR)). Genes assayed display a wide range of frequency, from absent to fixed for the favorable allele/haplotype Fig. . For BlaMagnaporthe oryzae under controlled conditions. Based on phenotypic measurement, the most virulent isolate was M64-1-3-9-1 and the least virulent was CA89. A wide variation in the resistance to the five blast isolates was found in the ECP with most of the genotypes displaying intermediate resistance to one or more isolates  or absent  were excluded from this analysis. No single allele or combination of alleles was found to perform consistently better across the five isolates. However, the presence of Pi-ta alone or in combination with Ptr, Pi5/Pii, Bsr-d1 or Pi2/Pizt tended to be associated with more resistant phenotypes. For example, the genotype IR12A311 that carries Pi-ta, Ptr and Pi5 was found to be the most resistant across the five isolates. The combination of Pi-ta and Pi2/Pizt was found in IR93346:1-B-13-7-6-1RGA-2RGA-1-B, a line resistant to 4 isolates. Further, six genotypes  among the ten most resistant carried at least Pi-ta. IR11A341, which contains Pi33, was also highly resistant to three isolated and was part of the ten most resistant lines across the five isolates. Further characterization of Pik-h and Pik-m would be useful to refine the classification of the lines and understand the pattern of resistance.The ECP lines were evaluated for their level of resistance against five isolates of Xa4 allele was present in most of the ECP material. However, it  did not significantly reduce the symptoms compared to the susceptible check (IR 24) or the ECP lines without a known favorable allele for BLB genes . Xa7 (one genotype) and Xa21 were also found in more resistant genotypes across most of the isolates. The five most resistant ECP genotypes across the 14 isolates, with similar values to the IRBB checks, were IR15A4029 , IRRI154 , IR12N252  IR 100,097-B-B RGA-B RGA-8  and IR12N249 .ECP lines were also screened for resistance to bacterial leaf blight infection against 14 known isolates. The two most virulent isolates were PXO 340 and 99 with an average lesion length of 21.9 cm and 21.3 cm, respectively  from 1960 to 2014. The estimated rate of genetic gain when the data set was restricted to only IRRI released varieties was estimated to be 17.36 kg·ha−1 year−1 (0.46%) and 186.24 kg·ha−1 cycle−1 (4.95%). Previous reports of genetic gain in this program using an era study with a selection of 7 and 12 released varieties have shown gains of 81 and 75 kg·ha−1 year−1 (~ 1%), respectively  and 25 kg·ha−1 year−1 (0.87%) using 214–242 advanced breeding lines  over a 26-year period using a meta-analysis of 376 advanced breeding lines . Similar estimates for irrigated rice in Brazil were reported using rapid-cycle recurrent selection and data from 667 selection candidates that were progeny tested in different breeding cycles  was estimated to be 8.75 kg·haThe contemporary program has moved to a much more intensive recurrent selection strategy based on quantitative genetics principles to drive genetic gain for yield in the context of a disease resistant and high grain quality genetic background. This approach is a natural progression building on previous eras where the focus was on the identification and integration of genetic variation for yield potential traits  of 22. This value may be underestimated as the markers used for the analysis were specifically designed to be informative in the indica subpopulation and harbored high minor allele frequencies on average  in the population, and the selection intensity , and so represent diversity that is easily selectable in the existing breeding populations; these include Pita, Ptr, Pii, BPH32, sweet13, xa5 and TSV1.Many traits in rice are governed by a number of high value, large effect alleles that affect patterns of phenotypic variance across environments  is the most effective combination conferring broad-spectrum resistance for bacterial blight. While 30% of the ECP lines contained one or more bacterial blight resistance alleles, none of the ECP lines contained this specific combination as xa13 in particular is at a frequency of zero. However, it is clear the gene combinations that are present offer resistance to all tested isolates except PXO 99. PXO 99 is a common Xanthamonas isolate in the Philippines  some strategic cautions are warranted. Product development is the primary goal of a breeding program, however when product development is strongly emphasized, it is tempting for breeders to overuse specific high-value lines in a crossing block, at the expense of gene pool management. While every cross may not bring together the complete package necessary for a product release, specific crosses generated with the intention of creating progressively improved allelic combinations (and slowly increase in frequency of major genes) can generate useful lines that can be recycled into the crossing block as parents. Likewise, the use of haplotype-matched backcrossing donors of key genes can be a powerful tool for introducing novel alleles that are at zero or low frequency into the elite gene pool while preserving the availability of genetic variance immediately around them for recombination and the improvement of quantitative traits.Managing the deployment of single genes within the breeding program due to limitations imposed by their frequency within the ECP is not the only consideration future breeding efforts based on this germplasm resource must consider. Since quantitative traits are not governed by single genes, location main effects and genotype by environment interactions must be routinely accounted for in phenotypic analysis strategies to factor out their strong influence on phenotypic outcomes. Because the primary source of data for determining the breeding values for yield that were used to identify the ECP were trials conducted in the Philippines, an understanding of yield performance in other target environments was also necessary. In order to evaluate the performance of the ECP lines relative to local and IRRI checks in relevant geographies outside the Philippines, six breeding trials were conducted in India, Kenya, and Tanzania. BLUP values centered on the mean performance of each location indicate strong performance of the ECP lines relative to the highest yielding local checks in each location. The performance relative to IRRI varieties and the local checks is a strong indication that the observed genetic variance is manifested as phenotypic variance within each location, indicating that crossing and selection among high performing lines within each breeding zone is likely to result in genetic gain for yield. Further analysis of genotype x environment interactions and the genetic correlations between target environments is warranted to help identify a global testing strategy that best leverages limited public resources available to the IRRI breeding program.−1 year−1 (0.46%) for released varieties. This rate of gain appears to be largely limited by long cycle times and the re-introduction of old material or landraces into the elite pool. This observation highlights the need to optimize the breeding strategy for quantitative traits by using quantitative genetics principles to get closer to the annual 1.5% gain in grain yield needed to cover the expected increase in rice consumption. The elite core panel identified and characterized in this study is a key component of this optimization. Indeed, recurrent selection with short cycles based on elite-by-elite crosses implemented at IRRI to deliver a higher rate of genetic gain for grain yield requires careful management of the genetic diversity, which starts with a comprehensive characterization of the most elite germplasm.Achieving short- and medium-term genetic gains for yield is a key target for almost every breeding program. In the case of the IRRI’s breeding program for irrigated systems, the rate of genetic gain for grain yield was estimated at 17.36 kg·ha2. We considered an environment as the combination of location, year and season. The environments considered varied in experimental designs according to either a row-column, alpha-lattice, augmented randomized complete block, or ordinary randomized complete block design (RCBD). A total of 102 studies were conducted in 23 environments with a total of 17,216 lines from which 15,286 were sorted out as irrigated rice lines data after filtering .All the yield data from trials conducted by the irrigated breeding program during the period 2012–2016 across multiple locations were retrieved from the IRRI database. From these trials, the following phenotypic information was extracted: plant height, number of days to flowering, grain yield and number of hills per plot. The phenotypic information extracted were filtered based on the following quality criterion: presence of a randomized experimental design, percentage of missing data for grain yield and flowering time lower than 15%, and harvested area greater than 2 mij represents grain yield for ith observation, μ is the overall mean, gi is the random effect of ith genotype with iid gi ∼ N where σ2g is genetic variance and εij is the residual error with iid εij ∼ N. To account for heterogeneous error variance caused by differences in the numbers of hills harvested from plot to plot and from trial to trial, the diagonal of R was set to h/hmax where, h is the number of hills harvested and hmax is the maximum number of hills harvested in the environment. The … in the model denotes the blocking factors and a covariate for missing hills which were conditional to the trial. These terms were included in the model because they were identified as improving model fit during analyses of individual trials. Blocking factors were considered random if they had more than five levels. The possible blocking factors were modelled to determine which factors led to the lowest Bayesian information criterion  where σ2g is the genetic variance and A is the additive genetic relationship matrix based on pedigree, ej is a fixed effect of the environment j, εij is the residual error with εij ∼ N where R is a matrix proportional to the residual error covariance matrix and σ2ε is the error variance. To account for heterogeneous error variance, the diagonal of R was 1/r2. In the above model yield was adjusted using days to flowering as covariate in the model. The R packages lme4  were classified as non-elite material and the remaining part as elite material.pedigreemm  the lines were filtered based on high ranking for their grain yield breeding values. Genetic relationship for the genotypes was measured by calculating the coefficient of parentage (CoP) based on the pedigree information using R package Ne) of ECP was calculated using SNP data. Further, frequency of major trait genes was estimated in the ECP.Genetic diversity of ECP and its relationship with the 3000 rice genomes  at vegetative growth stage (28–35 days old) plants. DNA was isolated and purified according to the modified Cetyltrimmethyl Ammonium Bromide (CTAB) protocol  or a HiSeq 2000 system . Resequencing data was filtered and trimmed for low-quality base calls using standard pipelines, and mapped to the MSU7 build of the Nipponbare genome. Based on sequencing data quality, four lines were discarded. Base calls at specified informative positions were generated using SAMtools . First, the physical position coordinates of the 703 filtered 1k-RiCA SNPs were used to extract the filtered set of markers from the 3K-RG using the rice SNP-Seek database  was estimated based on linkage disequilibrium (LD) information between the markers (Hill r2) is expected r2 for a marker distance c. Pearson's squared correlation coefficient (r2) of each pair of loci as a measure of LD in relation to physical distance was calculated in the R package sommer , the bacteria causing bacterial leaf blight disease (BLB)) were assessed in controlled conditions. The pathogens for both blast and BLB were obtained from isolates collected in the Philippines. In addition to the evaluations in controlled condition, agronomic traits were evaluated in multi-environment experiments located in the target regions of IRRI’s irrigated breeding program.ECP accessions were evaluated for disease resistance to two important pathogens  and four blast resistant checks  were evaluated. The experiment was set by planting test genotypes in the screen house trays at IRRI, Los BañosMagnaporthe oryzae isolates, M101-1-2-9-1, M64-1-3-9-1, Ca89, BN111 and IK81-25 were selected based on their reported differential disease spectrum on blast monogenic lines carrying blast resistance genes Pi54, Pi9, Pi-ta, Pi-km and Pi2. These Isolates are part of the set of 20 standard differential blast isolates in the Philippines. They were selected due to their good sporulation and ability to differentiate the differential varieties. Further, they have been used over time in rice variety selection experiments and their pathogenicity has remained stable , IRBB60  and IRBB62 ) and a susceptible cultivar (IR24). The experiment was conducted under greenhouse conditions in systematic arrangement with two replications at IRRI, Los Baños, Philippine in 2018. 28 minoru trays (two trays per strain) were prepared. Crop management was performed according to rapid generation advancement protocol  were used, namely: PXO16, PXO86, PXO79, PXO340, PXO71, PXO112, PXO99, PXO145, PXO280, PXO339, PXO349, PXO347, PXO363 and PXO34. The Xoo strains were sourced from IRRI laboratory stock and revived using modified Wakimoto’s medium for 48 to 72 h at 28 °C. Inoculum preparation was implemented according to Goto (Fourteen Philippine strains (PXO) representing 10 races of  to Goto .th day after seeding. Plants were inoculated with one strain of Xoo on the 45th day after seeding using a leaf-tip clipping method. Three leaves per plant were inoculated. Evaluation of resistance was done at 14 days after inoculation (DAI) by measuring the lesion starting from the point of inoculation to the end with visible symptoms. For a given isolate, the genotypes with less than four measurements (out of six) were removed from the analysis. The average lesion length was computed for all the ECP lines and the checks. The final dataset had 2.9% missing data and is available in Additional file The accessions were prepared for inoculation by punning the lower leaves and extra tillers on the 35Phenotypic characterization of ECP for grain yield was conducted in Kenya, Tanzania, India and the Philippines. Different experimental designs such as randomized complete block, alpha-lattice and partially replicated designs were used across these environments  and days to 50% flowering time (days) from all the ECP trials were used in this study. The best linear unbiased predictor (BLUP) values were calculated using the Additional file 1: Table S1. List of all lines 15,286 of the irrigated breeding program selected for the analysis of genetic gain. The following information are available: designation (name of the lines), GID (identification number), parentage, year of the initial cross, type of entry (breeding lines or IRRI released varieties), ECP (belong or not to the elite core panel), equivalent complete generation (EqG), breeding value for yield corrected from flowering time. Table S2. Genotypic data for 66 ECP and 10 IRRI varieties from the breeding program on 703 SNP from the 1k-RiCA platform. The data are in hapmap format. Table S3. Information on the 37 high-value genes related to abiotic stresses. The favorable allele frequency is reported for 33 genes which had sufficient data. Table S4. Combined genotypic data of ECP and 3K-RG indica accessions formatted as a dosage matrix with SNP genotypes coded as 0, 0.5, 1. Table S5. Results of the evaluation of the elite core panel against blast disease. The average infection score to five highly virulent Magnaporthe oryzae isolates measured in controlled conditions are reported along with the favorable alleles (if any) for each line. The resistant and susceptible checks are also included. Table S6. Results of the evaluation of the elite core panel against bacterial leaf blight. The average average lesion length (based on six plants) to fourteen isolates of Xanthomonas oryzae pv. oryzae measured in controlled conditions are reported along with the favorable alleles (if any) for each line. The resistant and susceptible checks are also included. Table S7. Studies conducted in the target regions for IRRI's irrigated rice breeding program to evaluate the performance of the elite core panel (ECP) lines in 2019.Additional file 2: Fig. S1. Evolution of equivalent complete generation (EqG) of parental lines used between 1985 and 2014 for IRRI’s breeding program for irrigated systems. Fig. S2. Distribution of the infection score (0 = highly resistant to 5 = hihgly susceptible) for five isolates of blast (Magnaporthe oryzae). The elite core panel (ECP) lines are in grey, the susceptible checks (Lijiangxintuanheigu and CO 39) are in orange and the resistant checks  in green. Fig. S3. Scatter plots and rank correlations between blast isolates using infection scores. Fig. S4. Distribution of the lesion length of elite core panel (ECP) lines after controlled inoculation with Xanthomonas oryzae pv. Oryzae. Fourteen different isolates were used to assess the level of resistance of the ECP lines. The ECP is in grey, the susceptible check (IR 24) is in orange and the resistant checks  in green. Fig. S5. Scatter plots and rank correlations between bacterial leaf blight isolates using the average lesion length for each genotype. Fig. S6. Correlation matrix for grain yield (A) and time to flowering (B) for all the environments where the ICP lines have been evaluated.


Common bean is a nutritious food legume widely appreciated by consumers worldwide. It is a staple food in Latin America, and a component of the Mediterranean diet, being an affordable source of protein with high potential as a gourmet food. Breeding for nutritional quality, including both macro and micronutrients, and meeting organoleptic consumers’ preferences is a difficult task which is facilitated by uncovering the genetic basis of related traits. This study explored the diversity of 106 Portuguese common bean accessions, under two contrasting environments, to gain insight into the genetic basis of nutritional composition  and protein quality (amino acid contents and trypsin inhibitor activity) traits through a genome-wide association study. Single-nucleotide polymorphism-trait associations were tested using linear mixed models accounting for the accessions’ genetic relatedness. Mapping resolution to the gene level was achieved in 56% of the cases, with 102 candidate genes proposed for 136 genomic regions associated with trait variation. Only one marker-trait association was stable across environments, highlighting the associations’ environment-specific nature and the importance of genotype × environment interaction for crops’ local adaptation and quality. This study provides novel information to better understand the molecular mechanisms regulating the nutritional quality in common bean and promising molecular tools to aid future breeding efforts to answer consumers’ concerns. Phaseolus vulgaris L.), is an important and affordable source of protein, dietary fiber, essential vitamins, and minerals [Consumers are increasingly more health-conscious and striving to have greater diversity and healthier foods in their diets . Common minerals . It is ominerals , being aminerals , and a cminerals . Consequminerals , as wellminerals ). In addminerals .Despite its compositional richness, common bean production and consumption have been declining in Europe. Several factors contributed to this, such as low productivity, changes in consumers’ preferences, and little investment in breeding and food innovation . NeverthThe complexity of food nutritional quality breeding comes also from potential hidden interactions among the quality-related compounds. In particular, legumes possess bioactive compounds which can act simultaneously as health-promoting compounds and anti-nutritional factors, impairing nutrients’ bioavailability, and influencing both taste and consumers’ food acceptability . ProteasA common aspect of the mentioned nutritional quality-related targets is that many of these nutritional compounds’ contents are laborious and expensive to measure and so difficult to routinely implement in breeding programs. However, the use of genomics-assisted breeding allows a considerable time and cost reduction in the development of crop varieties with improved nutritional contents as compared to conventional breeding . NeverthGenome-wide association studies (GWAS) using populations of unrelated individuals to examine associations between genotypic polymorphisms and phenotypes are presently regarded as a good alternative to linkage mapping approaches to identify quantitative trait loci (QTL) responsible for complex traits variation . CoupledMore than five centuries of bean cultivation in Portugal have produced a very diverse germplasm. Indeed, Portugal is considered a secondary center of origin of common bean diversity . This geThe present study aims to identify the genomic regions and/or candidate genes associated with common bean nutritional composition  and protein quality (amino acid contents and trypsin inhibitor activity) within a diverse Portuguese germplasm collection, using a GWAS approach. We will test SNP-trait associations using linear mixed models accounting for the genetic relatedness between accessions and compare the SNP-traits association profile under two contrasting environments. By incorporating a heat stress environment in the study, we will be able to assess QTL stability under an expected climate change scenario for future exploitation of potential genotype × environment (G × E) for local adaptation. This study will also be useful for the development of molecular tools to facilitate routine evaluations on nutritional composition and protein quality, increasing the efficiency of common bean breeding for improved nutritional quality. The present study was carried out to clarify the genetic architecture of nutritional composition and protein quality-related traits in common beans by making use of the Portuguese germplasm natural variation. For that, previously collected data  on the nThe total phenotypic data analyzed included nutritional composition related traits such as macronutrient contents , fat, fiber, ash, moisture, and resistant starch (RS)), and protein quality-related traits such as amino acids contents , and trypsin inhibitor activity (TIA). Traits regarding nutritional composition were measured in the two contrasting environments, whereas protein quality-related traits were only measured in the samples harvested in the most stressed environment, Córdoba.TM assay and DArTseqTM analysis [This phenotypic data was then integrated with previously obtained single-nucleotide polymorphism (SNP)-based genotypic data , screened through Illumina Infinium BARCBean6k_3 BeadChipanalysis . GenomicMost nutritional composition-related traits showed a similar range of phenotypic variation between environments . NeverthOf the traits measured only in Córdoba, trypsin inhibitor activity showed the highest variability (29.6%). Among the amino acid contents, Methionine stood out showing the highest variability (21.2%). The remaining amino acid contents had all very similar coefficients of variation. The amino acid present, on average, in lower amounts was Methionine (1.06 g/100 g) and the one present in higher amounts was Glutamic acid (20.44 g/100 g) .Variance components were estimated for the nutritional composition-related data obtained from the Córdoba and Cabrela trials taken together. A high influence of the environment (E) was observed for the majority of the traits . The effTraits’ broad-sense heritabilities  were, inA strong negative correlation between carbohydrates and protein , two principal component analyses (PCAs) were computed: one with the nutritional composition data from both environments, and another with the nutritional composition and protein quality data measured only at Córdoba environment.The PCA with nutritional composition data collected in Cabrela and Córdoba environments A depicteThe second PCA B includeAccessions adjusted means for each trait were tested for association with SNP data taking population structure or familial relatedness into consideration. Manhattan plots depicting GWAS results for protein are shown in 10  = 3. The conservative Benjamini-Yekutieli p-value adjustment supported the significance of 151 (67%) of these associations  was an exception to this, with markers explaining up to 70.6% (DART04597) of the observed phenotypic variance. Other SNPs explaining larger proportions of variance, besides the previously referred, were SNP01084 , DART02462 , SNP01413  DART11240 , and SNP00787  . The var2 > 0.2). This was investigated using the JBrowse tool in Phaseolus vulgaris v2.1 genome in Phytozome v12 portal. One hundred and two different candidate genes were identified within 136 genomic regions responsible for trait variation. A gene was considered a putative candidate for the phenotypic trait under analysis if it contained an associated SNP or was in linkage disequilibrium (LD) with an SNP associated with the trait, observing a strict LD-decay threshold  web tools to better understand the involvement of the candidate genes in different metabolic pathways. From the candidate genes identified, 41.7% had a functional category assigned . The assIn the frame of this work, it was not possible to highlight all candidate genes located within the associated genomic regions in detail. We, therefore, restrict ourselves to highlight those that were (1) located within regions associated with multiple quality-related traits, and with (2) a biological annotation related to the studied trait. The candidate gene associated with the biggest number of quality related-traits, which also had a biological annotation related to the traits, was Phvul004G045900, encoding a galacturonosyltransferase 9, which was associated with carbohydrates, protein, and moisture (in both environments). Another candidate gene highlighted due to its association with more than one quality-related trait was Phvul010G13440, encoding a hydroxyproline-rich glycoprotein family protein, which was associated with protein and carbohydrate contents. Finally, the genes highlighted due to their biological annotation were Phvul004G056800, encoding an ankyrin repeat family protein, Phvul009G061400, encoding a cytochrome P450, family 82, subfamily C, polypeptide 4, and Phvul002G113000, encoding a transmembrane amino acid transporter family protein, respectively associated with resistant starch, ash, and Arg.This study explored the natural variation of 106 accessions from the highly diverse and underused Portuguese common bean germplasm grown under contrasting environments , using a GWAS approach to unveil the genetic architecture of 24 nutritional compositions and protein quality-related traits. The generated knowledge will allow a better understanding of the molecular mechanisms and pathways regulating the nutritional composition and protein quality in common beans. Further, it will assist the development of promising molecular tools to help breeders answer consumers’ diet concerns and to support farmers to better exploit G×E quality interactions under future climate constraints. A total of 136 common bean genomic regions controlling the natural variation of the analyzed traits were identified in this association panel. Additionally, 102 putative candidate genes for the trait-associated regions were proposed.SNP marker-trait associations were identified for all the nutritional composition and protein quality-related traits analyzed except for Glu, Gly, His, Ile, Leu, Met, Pro, and Thr. A mean of 9.5 marker-trait associations was identified per trait, each SNP explaining, on average, a small percentage of the phenotypic variation (around 16%). Mapping resolution to the gene level was achieved in 55.9% of the cases (LD blocks where a single gene was identified), demonstrating the complex genetic nature of common bean nutritional quality-related traits and validating the use of GWAS to harness the diversity of this Portuguese common bean germplasm.The protein content is one of the most relevant food grain legume traits for breeding, as the interest in plant-based protein increases in developed countries to provide healthier diets, and the need for cheap protein sources to fight malnourishment in developing countries remains ,29. PrevSeveral of the markers associated with protein content were simultaneously associated with carbohydrate content (in both environments) but with contrasting effects on trait variation, reflecting the expected strong negative correlation between these related traits. One of these markers was SNP04726, which was the second most significantly associated with protein content (explaining 9.4% of the variability) and was also associated with carbohydrates content, explaining 10.8% of its variability . A candidate gene including this marker sequence was Phvul.010G134400, which encodes a hydroxyproline-rich glycoprotein family protein (HRGP). HRGPs are known to accumulate in cell walls as a general response of dicotyledons to infection by biotrophic and necrotrophic fungi, bacteria, and viruses . In partApart from the genes associated with both protein and carbohydrates contents, very few other SNPs/candidate genes related to nutritional composition were connected to more than one trait, reflecting the small correlations among most nutritional composition-related traits. Indeed, most SNPs and subsequent candidate genes for all the studied nutritional quality-related traits were only associated with one trait, as can be seen in the network analysis of candidate genes .Of note, among the genes connected to more than one trait, is Phvul.004G045900, which encodes a galacturonosyltransferase 9. Two markers led to the identification of this gene, DART03724 which was associated with carbohydrate contents measured in seeds from Cabrela and moisture contents measured in both environments, and DART06845 which was associated with protein content measured in seeds from Cabrela. This gene was the only identified candidate associated with more than three traits, the only gene connected to the same trait in both environments, and with a corresponding variant allele responsible for the largest effect on trait variability for carbohydrates, protein, and moisture (data from Córdoba). Galacturonosyltransferases are required for the synthesis of pectin, a family of complex polysaccharides present in the cell walls of all land plants . MoreoveMoisture measured in seeds from Cabrela showed the biggest number of marker-trait associations detected (62) and the largest percentage of variation explained by a marker (70.6%). However, a significant portion of the associated SNPs was located within the same LD blocks, as 62 SNPs were located within 36 genomic regions. Two chromosomes, Pv07 and Pv03, are of particular interest and could be responsible for most of the observed variation. Pv07 contains one genomic region which includes the marker responsible for 70.6% of the moisture phenotypic variation. Pv03 contains most of the markers associated with moisture (43) as well as the marker associated with this trait at the highest statistical significance. Most of the associated markers within this chromosome (37) were enclosed within a relatively small section of the chromosome (3Mb) and were thus relatively close physically. Despite the physical closeness, 25 genomic regions (LD blocks) were still detected within this section of the chromosome suggesting that various QTL may be responsible for the phenotypic variation of moisture within this chromosomal region. Moisture from Cabrela contrasted with most other traits in the amount of explained variability per QTL, including moisture measured in seeds from Córdoba. In the Córdoba environment, moisture was associated with six markers, each explaining less than 9% of the phenotypic variance. The difference in the proportion of variance explained between environments was mainly due to higher effects of each marker on the phenotypic variation in Cabrela (up to 0.7) than in Córdoba (up to 0.3). This difference might indicate some inflation of the effect attributed to each marker. This inflation could be explained by the relatively small mapping population used since smaller populations lead to inflated QTL effects .QTLs for ash, fiber, and starch in common bean were also identified through linkage mapping in the previously mentioned study of Casañas et al. . In thatArabidopsis thaliana, as was proposed by Murgia et al. [Also associated with ash content was the SNP03984 in Pv09. A candidate gene for this associated SNP is Phvul.009G061400 which encodes a cytochrome 450, family 82, subfamily C, polypeptide 4 (CYP82C4). CYP82C4 is a heme-containing enzyme that is strongly correlated with genes involved in metal uptake/transport and is possibly involved in the early iron deficiency responses in a et al. . The inva et al. .10 (P) threshold.The strong correlations among amino acid contents were reflected by the association of several SNPs to more than one of these traits. As an example, four molecular markers and the respective candidate genes were associated with three different amino acid contents: DART01478, SNP00739, SNP00741 were associated with Asp, Phe and Tyr; and SNP04308 was associated with Ala, Lys, and Val contents. Interestingly, Arg was the only amino acid content that did not share SNPs and subsequently candidate genes with other amino acid contents. Of the several SNP markers associated with Arg, the one within the candidate gene Phvul.002G113000 was the most strongly associated and the one that explained the highest proportion of variance (20.2%). This gene encodes a protein of the transmembrane amino acid transporter superfamily. These are integral membrane proteins involved in the absorption of amino acids from the soil, load and transport of amino acids in the phloem, absorption of amino acids in seeds, and long-distance transport and distribution of amino acids in seeds . FunctioAs has been previously shown, the environment, in particular heat and drought stress, influence the maturing process of seeds, affecting various nutritional quality traits in the process (reviewed in ). AccordIn addition, G×E was also significant on the traits analysed in both environments, particularly for ash and fat contents. G×E occurs when genotypes differ in their relative performance across environments and corresponds to the presence of genetic factors with environment-specific effects . The magAs previously stated, DART03724 with Phvul.004G045900 as a putative candidate gene, was the only molecular marker/gene associated with the same trait (moisture) in both environments. The lack of more markers stably associated with traits across environments is a consequence of the environment-specific nature of the genomic regions associated with the studied traits. Constitutive QTLs, which show consistent effects across environments, are the main targets for breeding programs as they can improve crop performance across various regions where the crop can be grown. Nevertheless, it is possible to take advantage of significant G×E by breeding for local adaptation  and alsoUnder these more stressful conditions, particular Portuguese accessions stood out as promising sources of protein quality-related traits. Traits such as bioactive compounds that influence protein digestibility and limiting amino acids are factors used to determine protein quality . LimitinDue to the detailed and expensive nature of the analysis developed to measure some of the nutritional quality traits, the size of the population and the number of tested environments had to be restricted in the present study. Smaller populations lead to lower QTL detection power as well as inflated estimates of QTL effects . NeverthIn conclusion, this study further characterized the Portuguese common bean germplasm through the clarification of its genetic architecture of nutritional quality traits. The functional categorization of the proposed 102 candidate genes for 24 nutritional composition and protein quality-related traits demonstrated the involvement of a variety of metabolic pathways in the determination of common bean nutritional quality, corroborating the genetic complexity of these traits. Additionally, this study provided a unique resource of molecular markers associated with common bean nutritional and protein quality traits, which will help to answer consumers’ nutritional demands as well as broader vs. local adaptation on future breeding efforts. In particular, the inclusion of data from Córdoba, a heat stress environment, allowed the identification of markers relevant for quality breeding in a context of climate change with increasing temperature scenarios, such as the one being experienced in the Mediterranean area.A collection of 106 common bean accessions, from the Portuguese plant germplasm bank  was used for the present genetic study. This collection was the same as described by Leitão et al. ,27 to idSeeds from the bean accessions were sown in two different environments following a randomized complete block design, with two replicates at each environment, as described by Mecha et al. . The fieTwenty-three traits related to nutritional composition and protein quality were measured in the common bean harvested from the two field trials as described by Mecha et al.  and retrBriefly, Mecha et al.  determinA LC-MS/MS system Waters Alliance 2695 HPLC system coupled to a triple quadrupole mass spectrometer, Micromass® Quattro micro API , equipped with an electrospray ionization source (ESI) was used to determine the content of the 16 amino acids . ProteinThe chromatographic separation was performed in a Mediterranean Sea 18, 5 µm 20 × 0.21 cm, 1.8 µm,  column. The amino acids were analyzed by multiple reaction monitoring (MRM) mode, using an ESI source operating in ion positive mode. Amino acids were identified by comparison with the amino acids’ standard retention time and corresponding m/z values.Trypsin inhibitors were extracted from 0.5 g of common bean flour to which 25 mL of NaOH 0.01M were added, and the pH was adjusted to 9.5 ŷ 0.1. Inhibition percentage and trypsin inhibitor activity were calculated according to Mecha et al.  and AOACQuality control of phenotypic data was performed separately for each environment. Technical repetitions were averaged for each accession to minimize technical error. A descriptive statistical analysis was performed using the summary statistics option of Genstat® software, 21st edition . HistogrA linear mixed model was fitted per trait as trait = genotype + block + error for the analysis of traits measured in a single environment and as trait = genotype + environment + genotype x environment + block + error for the analysis of traits measured in both environments using the restricted maximum likelihood (REML) variance component analysis framework of Genstat software, where environment identifies the two field trials and block identifies the two plot replicates within each trial.Models were initially fitted with all terms as random to obtain the best linear unbiased predictors (BLUPs), estimate variance components, broad-sense heritability and Pearson correlation coefficients between traits. In a second step, genotype and block were fitted as fixed terms to obtain the best linear unbiased estimates (BLUEs), for each trait and accession. BLUEs were determined for fat with genotype and environment fitted as fixed terms. Wald tests for the significance of fixed effects were performed. BLUEs were used for principal component analysis (PCA) and as input phenotypic data for GWAS.To estimate how much of the variation of accessions’ nutritional composition could be explained by the environment or the interaction with the environment, the previously defined model was fitted considering environment and genotype × environment as fixed terms, and a Wald test was performed to test for the significance of the fixed effects.Genome-wide association studies were conducted for all the 24 traits using the QTL library procedures from Genstat software. Adjusted means (BLUEs) for each trait were tested for association with a previously collected genotypic dataset  retrievep-values with lower deviations from the expected uniform distribution under the null hypothesis were the considered parameters to select the best model accounting for genetic structure/relatedness among genotypes.GWAS was performed separately for Cabrela and Córdoba environments for the traits that showed a higher variance component of genotype by environment (GxE) interaction than of genotype (G). Otherwise, the association study was performed with data from both environments together. GWAS was performed in the mixed model framework of Genstat software, fitting SNP as fixed and genotype as random terms using REML . Four mo10  of each SNP was plotted against its chromosomal position to produce a Manhattan plot. Significant SNP-trait associations were detected at a threshold of −log10  = 3. This threshold was set taking into consideration two aspects: the size of the association panel used and the background noise of the obtained Manhattan plots. Similar criteria were already described in other works with comparable panel sizes and a similar number of markers, focusing on resistance/tolerance traits [p-values following the Benjamini and Yekutieli (BY) false discovery rate (FDR) method [The observed −loge traits  to avoid) method  were cal) method ), to conQTL/Vpheno, where VQTL = 2freq(1-freq)effect2 and Vpheno is the phenotypic variance of the adjusted means of each trait [For every SNP significantly associated with a trait, the effect of the minor-frequency SNP variant was calculated. The proportion of variance explained by each SNP-trait association was estimated using the formula Vch trait .2 > 0.2). LD was previously calculated for each common bean chromosome using the squared coefficient of the correlation between marker pairs r2 [2 > 0.2 in relation to the associated SNPs were considered to be within the same LD block or genomic region. Putative candidate genes were searched for using the JBrowse tool in the Phaseolus vulgaris v2.1 genome , available at the Phytozome v12 portal [A gene was considered a putative candidate for the phenotypic trait under analysis if it contained an associated SNP or was in linkage disequilibrium (LD) with an associated SNP observing a strict LD-decay threshold (rpairs r2 , and ret2 portal . The annCandidate genes were also assigned to MapMan bins, which described biological contexts/concepts, using Mercator4 V2.0 . Cytosca


Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide significance (p < 6.25 × 10−10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation.Facial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed significant (p < 2.5 × 10 Genetic factors play an important role in shaping human facial features. Over the last decade, studies have identified numerous genes associated with various facial traits. The vast majority of these studies have focused on European or Asian populations, while African populations have been underrepresented. Increasing the diversity of these analyses can reveal novel associations and cross-population analyses can help deepen our understanding of known genetic associations. We therefore performed a genome scan of 3D facial features in African children from Tanzania and then compared our results to Europeans. We found 20 regions of the genome associated with facial shape in Tanzanian children, 10 of which were also present in Europeans, indicating evidence for a partly shared genetic basis for human facial shape across populations. In addition, about half of the genetic associations observed in Tanzanians were not present in Europeans, and some of the shared signals differed between populations in the specific genetic variants associated or specific facial traits affected. These results shed light on the shared and population-specific genetic contributors to normal-range facial variation. PAX3, TBX3, SOX9, PAX1, 4q31.3, 6p21.1, 20q12). African populations are particularly under-represented in facial GWA studies. The only previous GWAS of facial morphology in an African population was performed by Cole et al. using landmark-based phenotypes extracted from 3D facial images in 3,505 Bantu children and Mwanza adolescents ) SNP-level testing (via linear regression) of the projection of the European-ancestry dataset onto the Tanzania derived phenotype  as a univariate phenotype; (T2) SNP-level look-up for the "best segment" ; (T3) locus-level (+/- 500kb from lead SNP) look-ups for the "best segment"; (T4) SNP-level look-up for a qualitatively similar facial segment; (T5) locus-level look-ups for a qualitatively similar facial segment. The statistical analysis was implemented separately for each combination of genome-wide significant SNPs and corresponding facial segments.Direct replication testing was complicated by two features of the study design. First, the data-driven facial segmentation process and PCA of the quasi-landmarks used to generate the multivariate phenotypes are specific to each dataset. That is, the 63 segments are not exactly comparable across studies . Second,The projected phenotype approach (T1) ensures that traits being compared across the two cohorts are equivalent and provides a means of directly replicating in Europeans the same genotype-phenotype relationship identified in the Tanzanian GWAS. The SNP-level look-ups in qualitatively similar and “best” segments allows the effect of a variant to differ across African and European ancestry groups. The locus-level look-ups allows for genetic differences  across African and European ancestry groups. Cumulatively, these approaches (T1-T5) allow for detection of effects that meet different criteria for inter-ethnic replication. The projected phenotype approach (T1) provides the most direct means of replicating the same genotype-phenotype relationship identified in the Tanzanian GWAS, whereas the similar segment approach (T4) relaxes the need for the facial effect to be exactly identical. However, both of these approaches could miss replicating effects due to differences in LD structure across populations if the tested SNP is not actually causal. The locus-level approaches (T3 and T5) can accommodate detection of different causal variants, or different LD-based proxies of the same unobserved causal variant, across populations. The “best segment” approaches (T2 and T3) can accommodate detection of the associated variants that manifest differently across populations given the distinct facial morphologies in Europeans and Africans.−8 in the European population, whichever was greater, as the significant threshold for replication.For replication tests (T1—T5), the significance threshold after Bonferroni correction was determined as 0.05 divided by the number of GWAS signals tested for replication. For the best segment SNP/locus-level replication test (T2 and T3), this threshold was further divided by the number of independent facial segments (n = 40). For the locus-level tests (T3 and T5), we further divided by the effective number of SNPs  at the lFor loci showing genome-wide association in the Tanzania cohort, co-localization analysis was performed based on the association between the best facial segment in the Tanzania cohort and a comparable segment in the European sample. Co-localization analysis was performed using the “locuscompare” function in R.in silico replication tests : (T1) SNP-level testing of the projection of the Tanzania dataset onto the European derived phenotype; (T2) SNP-level look-up for the "best segment" and (T3) locus-level look-ups for the "best segment". The significance threshold after Bonferroni correction was determined in the same way as described in the replication analyses in the European meta-GWAS.The previous study in Europeans reported 203 significant associations with facial variation. To explore these associations in the Tanzania cohort, we performed three a priori landmark-based  and qualitative phenotypes . The associated variants from these studies are summarized in in silico replication tests (T2 and T3) using the same methods as described above for determining the significant threshold after Bonferroni correction.In addition to the previous studies in Europeans using the same data-driven global-to-local phenotyping approach as used here, there have been 14 GWAS and one whole-exome sequencing study, to date, using The data-driven global-to-local phenotyping procedure yielded 63 hierarchically arranged facial segments, as shown in −8) evidence of association with at least one of the 63 facial segments  . No predominant enrichment of H3K27ac signal was observed for other primary cell type/tissues. These observations are consistent with previous studies in Europeans [We explored the cis-regulatory activities of the 20 GWAS loci across more than 100 cell types/tissues. As shown in uropeans .We utilized the Roadmap Epigenomics Project data of human embryonic craniofacial samples to identify specific genetic elements that may play a regulatory role during embryonic development nearby the 20 GWAS signals . Among t2 > 0.8) proxy SNPs were available in the European dataset. The remaining nine SNPs were not available in the European cohort due to the low minor allele frequency in the European population. Seven of the 11 lead SNPs  replicated under the projected phenotype approach (T1), indicating consistent genetic effects in the projected African-trait facial segment  in the Tanzania sample, indicating consistent effects across different ancestry groups . The SNPBeyond testing specific lead SNPs for replication, we also explored evidence of association in the Tanzania cohort for all SNPs across the 203 signals in case different variants in these regions were associated in the different ancestry groups. For the candidate locus scan, 13 additional signals were associated under the significance threshold of 0.05 divided by 203 and then divided by the effective number of independent SNPs at each locus. However, these loci were associated with different facial traits  across the different populations, suggesting the presence of more than one regulatory element within a genetic locus potentially affecting different facial segments. The shared association signals in African and European populations are displayed in the Miami plot .a priori landmark-based and qualitative  phenotypes, of which five and 11 loci were replicated based on (T2) and (T3), respectively  surpassed our more conservative threshold for study-wide significance (p < 6.25 × 10−10). The locus 9p21.3 was not identified in the previous meta-GWAS of data-driven facial traits in Europeans, but a nearby SNP (~20Kb to the peak SNP in Tanzania) was identified in a GWAS of different traits derived from resemblance between siblings and projected into the same cohort of Europeans. The lead 9p21.3 SNP, rs10122939, is in the vicinity of the MLLT3 gene, a crucial regulator of human haematopoietic stem cells associated with acute leukemia. It remains unclear how the genetic findings of MLLT3 may relate to facial morphology. Another candidate at 9p21.3 is FOCAD, located ~350Kb downstream of the peak SNP, which is a potential tumor suppressor highly expressed in brain tissues. Haaland et al. identified a parent-of-origin interaction effect between FOCAD and maternal smoking contributing to cleft lip [In this study, we performed a GWAS of multidimensional facial traits in a sample of 2,595 unrelated healthy children and adolescents of African ancestry. We identified 20 genetic loci that were associated with normal facial variation at a genome-wide significance threshold of p < 2.5 × 10left lip . In ordeEEFSEC gene, suggesting that this SNP drives the association with nose and lip morphology in both populations  may play critical roles in craniofacial development. Further investigation is needed to establish their functionality and the mechanisms through which they impact normal facial variation.The other three loci showing study-wide evidence of association in Tanzanians, 10q26.11, 12q14.3, and 12q21.31, were reported by previous GWASs in European or Asian samples. Our findings revealed that these associations were shared across different ancestry groups. Together with functional evidence ,43–45, pCXCL14, which was associated with variation in the eye region, plays a critical role in ocular tissues during development [CXCL14 led to eyelid and mandibular defects in about a third of chick embryos, which is consistent with its expression in eyelid ectoderm and the first branchial arch [ZIC2 and ZIC5, was also associated with variation in the eye region. ZIC5 is expressed in the developing eyes in Xenopus embryos, and loss of zic5 in mice causes craniofacial anomalies [Xenopus models have shown that Zic5 protein is involved in the generation of neural crest tissue [In addition to the study-wide significant loci, we also identified 10 novel loci in Africans at genome-wide significance, some of which were near genes having known involvement in craniofacial development. For example, elopment –48. Knocial arch . We alsonomalies ,50. Moret tissue ,51. Becat tissue , it is uOf the 20 GWAS signals identified in the Tanzania cohort, seven showed associations in Europeans, suggesting trans-ancestry genetic effects on normal facial variation. Notably, these loci showed significant associations with the same facial segments among populations, which improved the reliability of the findings. For some of these loci, the GWAS in Europeans identified more global (broader) effects on the face compared to Tanzanians. For example, the peak SNP at locus 3q21.3 was associated with shape of nose and upper lip in Europeans, but only associated with the shape of nose in Tanzanians. The other 13 loci did not replicate in Europeans. For 11 of these, the top SNP had low allele frequency in the European cohort (MAF<0.01), suggesting these differences may be partly attributable to the allele frequency differences.Our GWAS in Tanzanians not only uncovered novel loci and candidate genes related to facial morphology, but also advanced our understanding of previously identified loci in Europeans. Several loci (such as 3q21.3 and 12q21.31) showed associations with facial morphology across different populations. However, due to the strong LD in Europeans, association was detected across a broad genomic region (~300Kb), posing a challenge for identifying the likely causal variant at these loci. In conjunction with the European results, co-localization utilizing the GWAS in Tanzanians provided a more fine-mapped association. Given human evolutionary history, African populations are characterized by a greater level of genetic diversity and less LD among loci compared with European populations. Because of the specific LD structure, the GWAS in Africans offered valuable insights relevant to the genetic factors that contribute to normal facial variation. That said, only a fraction of the loci originally identified in Europeans showed evidence of association in the Tanzanian cohort, which we postulate may be partly attributable to the population differentiation. Of the 203 European signals, more than half of the peak SNPs had substantial allele frequency differences between European and African populations (MAF difference >0.1), which would impact the power to detect associations. Furthermore, the low rate of replication may also be due to insufficient power in the Tanzanian sample due to the smaller sample size and the stricter p-value threshold for declaring significance. For these reasons, we caution that lack of replication across populations should not be taken as conclusive evidence that a signal is population-specific.As a whole, our results provided a glimpse into the developmental origins of facial variation. Given that CNCCs are a group of embryonic cells that give rise to most facial structures and arise at 3–6 weeks of human gestation, if the associations with facial shape captured by our GWAS are due to effects occurring early in embryogenesis then we expect activity of facial-associated loci to be observed in CNCCs. We not only showed the enrichment of facial-associated variants in CNCCs and embryonic craniofacial tissues, but also identified two facial-associated loci overlapping with putative craniofacial-specific enhancer activity. The regulatory elements lie in sequences highly conserved across vertebrates, indicating their possible functional importance. Specifically, the GWAS signal at 12q21.31 associated with facial variation in the eye region overlaps with a craniofacial-specific enhancer that is active beginning at Carnegie Stage 17, when the eyelids begin to form .Continuing challenges in researching the genetics of facial shape include the heterogeneity across diverse human populations, inconsistent phenotypic strategies, and the influence of environmental factors. We performed the same data-driven facial-segmentation phenotyping strategy in both Tanzanian and European cohorts, which leads to differences between studies in the exact facial phenotypes. Though overall quite similar, one major difference in the Tanzania facial segmentation was the emergence of an eye-related sub-quadrant, which was absent from the facial segmentation in Europeans. As a result of the presence of eye-related segments, we identified novel loci associated with facial shape around the eyes. The eye-related segmentation may be partly attributed to the different approach to collect 3D images. For the European cohorts, 3D facial images were obtained while participants’ eyes were fully opened during image capture, whereas for the Tanzania cohort, participants were not required to open their eyes while collecting the 3D image. To determine the effect of variation in open vs. closed eyes during imaging, we included the predicted open/closed state of the eyes as an additional covariate in our facial model and re-ran the segmentation and genome scans. Segmentation was largely similar with the exception of the eye-related sub-quadrant, which was absent in the eye-adjusted analysis. Likewise, genetic association results were similar, with the exception of signals specific to the eye-related modules. See 2 on facial variation, the sample may include some children whose faces are under- or over-developed for their chronological age. This deviation from the average growth trajectory may decrease the signal to noise ratio, thus reducing power to detect genetic associations, or may represent a timing-specific aspect of facial variation, possibly under genetic control. Despite this limitation, it is important to acknowledge that failure to completely account for the effects of age variation on facial traits is unlikely to result in false positive genome-wide signals.Another potential limitation of the study is the restricted age range of our sample, comprising mostly children and adolescents whose faces are still developing. While our covariate adjustments adequately accounted for mean effect of age and ageSCHIP1 and PDE8A with facial size, whereas in the present study we did not investigate size or allometry phenotypes; instead we adjusted our phenotypes for facial size. Moreover, the global-to-local phenotyping approach employed in this study was developed specifically because landmark-based phenotypes, such as those used in Cole et al. [This study is a re-analysis of the dataset originally reported in Cole et al.  and repre et al.  do not ae et al. . Taken tEEFSEC, SFRP2, EMX2, ALX1 and HMGA2, are biologically plausible candidates that play important roles in embryological facial tissues. In addition, we revealed 10 new genetic loci that passed the threshold for genome-wide significance, and which are candidates for future replication studies. These findings improve our understanding of genetic and biological basis underpinning the diversity of human facial structure and may offer valuable insights into to biological mechanisms responsible for craniofacial morphogenesis and dysmorphology. Additional genetic replication and experimental validation will be required to verify the handful of newly identified genes/loci with unclear roles in craniofacial development.While verifying the association of some previously reported loci, we provide new evidence that these loci contribute to facial shape variation across populations. Among these replicating loci, several genes, such as S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigColor-coded facial segmentations in Tanzanians (left) and Europeans (right). The entire face (red) is partitioned in to outer face (orange) and midface (cyan), and further partitioned in more localized regions representing the lower face (magenta), upper face , nose (blue), and mouth and eyes (green).(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigThree LZ plots represent the genetic associations in (a) the best facial segment in Tanzania, (b) a comparable facial segment in Europeans, and (c) the best facial segment in Europeans. (d) A colocalization plot between the Tanzania GWAS and European GWAS at the chromosome 1q22 locus; (e) the association results in the Tanzania GWAS; (f) the association results in the European GWAS. (d-f) The SNP indicated by the purple diamond is the SNP for which the LD information is shown, with African LD structure indicated in the colocalization plot. The vertical gray dashed lines indicate the p-values of SNPs from the Tanzania GWAS that were unavailable in the European GWAS; the horizontal gray dashed lines indicate the p-values of SNPs from the European GWAS that were unavailable in in the Tanzanian GWAS (g) A polar dendrogram showing the global-to-local effect in Tanzania GWAS. Facial segments with a p-value lower than the genome-wide threshold (p = 2.5 × 10−8) are circled in black.(PDF)Click here for additional data file.S6 Fig(a) regional association plot for the signal at 5q31.1. (b) UCSC genome browser custom tracks for the 5q31.1 region, in which the yellow colored bars represent enhancer activity and the green colored bars represent Tx (Strong_transcription).(TIF)Click here for additional data file.S7 Fig(a) regional association plot for the signal at 12q21.31. (b) UCSC genome browser custom tracks for the 12q21.31 region, in which the yellow/orange colored bars represent enhancer activity; the purple colored bars represent PromBiv ; the grey colored bars represent ReprPC (Repressed_PolyComb).(TIF)Click here for additional data file.S8 FigMiami plot showing (upper) Tanzanian and (lower) European GWAS results. The dashed blue line in each panel indicates the genome-wide significance threshold, and the red solid line indicates the study-wide significance cutoff. In each panel, the cyan and red colored points, respectively, represent signals showing locus-level and SNP-level evidence of replication in the alternate cohort.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.