Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA  accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine , leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels.Rice ( Rice is one of the most important staple foods, feeding more than half of the global population. It provides carbohydrates, amino acids, dietary fiber, etc., for the nutritional supplementation to humans ,2,3,4. OA genome-wide association study, as a powerful tool, is widely used in the genetic dissection of complex traits controlled by polygenes ,12,13. FOsAUX5 (which encodes the amino acid transporter) gene in the uptake of valine and leucine was proven in our previous study of neutral essential amino acid accumulation in rice grains [OsbZIP18 contributing to the BCAA levels was also reported in rice leaf [With the advances in technology and the low cost of metabolomics assays, tremendous progress has been made using the metabolite-based genome-wide association study (mGWAS) approach, which has been widely applied in the elucidation of the genetic and biochemical bases of the metabolite of interest ,26,27,28e grains . Moreoveice leaf . Even thindica accessions and 150 japonica accessions. Using the recently proposed 3VmrMLM model, the main-effect quantitative trait nucleotides (QTNs), QTN-by-environment interactions (QEIs), and QTN-by-QTN interactions (QQIs) associated with the BCAA accumulation were identified in the BCAA content datasets (one BCAA with two years and two replicates per year). The objective of this study is to identify the candidate genes that control the BCAA levels and are potentially involved in the gene-by-environment and gene-by-gene interactions in rice grains.To obtain new insight into the genetic basis underlying the BCAA accumulation in rice grains, a genome-wide association study (GWAS) was performed on a diverse panel of 422 rice accessions with 239,055 SNPs; the association panel contained 272 H2) for Val, Leu, and Ile was from 0.45 to 0.82  contents of 422 rice accessions were qualified for the analysis of phenotypic variation. As a result, a wide range of variation coefficient (CV) was observed from 53.65% to 219.03% across the 12 BCAA content datasets  . Given t to 0.82 . Additioic panel A–C. For ic panel D. These indica accessions and 150 japonica accessions. An ADIMIXTURE-based population structure analysis and neighbor-joining (NJ) tree-based phylogenetic analysis also showed identical results  showed that these accessions were classified into two groups A,B, whic results C,D. To iradually E. In addr = 0.66) between the NFA and BCAA contents was observed in the Ile dataset (Ile_2012r1)  of the QTN-sf0130120297  locus  of the QTN-sf0608765681 locus (n = 215) of LOC_Os01g52530 with the GG allele showed a significantly higher Ile level than the accessions carrying functional allele1  (n = 363) of LOC_Os06g15420 with the TT allele showed a significantly higher Ile level than the accessions carrying functional allele1  . The phenotypic variance explained (PVE) by each QTN ranged from 0.41% to 9.20%. Of these QTNs, 113, 125, and 125 QTNs were identified in the Val, Leu, and Ile content datasets, respectively . Moreove_2012r1) . For the_2012r1) . A KEGG _2012r1) . Furtherp) locus . The LOC81 locus . The accn = 207) . Likewis n = 59) .LOC_Os03g24460 in the LD block  of the QEI-sf0314026224 locus encoding aminotransferase-domain-containing protein was related to the content variation of the BCAA Val (n = 222) and functional allele2 . A significantly higher Val content was observed in the accessions with allele1 than in the accessions with allele2 in the 2012r1, 2012r2, and 2013r1 datasets  and inflorescence (P2)  of the QEI-sf0132146191 locus encoding AMP-binding enzyme was identified as the key gene associated with the content alteration of the BCAA Leu (n = 376) and functional allele2 . The accessions carrying allele1 exhibited a significantly higher Leu level than those carrying allele2 in the 2012r1 and 2013r1 datasets  and seed (S2) based on the ePlant expression data  of the QEI-sf1219051313 locus encoding phosphoserine phosphatase protein was identified as the candidate gene responsible for the content alteration of the BCAA Ile (n = 39) and functional allele2 . The accessions carrying allele1 showed a significantly lower Ile content than those carrying allele2 in the 2012r1 dataset (LOC_Os12g31820 was observed in seed (S2), inflorescence (P6), and young leaf based on the ePlant analysis  C. AdditiBCAA Leu , which hdatasets B. LOC_Osion data C. AdditiBCAA Ile , which p dataset B. A highanalysis C.QTN-by-QTN interaction (QQI) detection is an important and complementary approach for the full genetic view of the BCAA accumulation in rice grains. Using the 3VmrMLM model, a total of 10 QQIs were detected in two BCAA content datasets . Of thosOsbZIP18 (LOC_Os02g10860) and OsMCCA (LOC_Os12g41250) in the QTN-sf0205661008 and QTN-sf1225461839 spanned regions were detected in the Leu_2013r2 and Leu_2012r1 content datasets (LOC_Os08g32850 and OsAUX5 with the gene ID LOC_Os11g06820) around the QTN-sf0820240591 and QTN-sf1103421417 were identified in the Val_2012r2 and Val_BLUP datasets  and LOC_Os06g15420 (KEGG annotation: asparagine synthase in biosynthesis of amino acid pathway) related to the Ile accumulation in grains was uncovered in this study. The biological function of ribose-phosphate pyrophosphokinase and asparagine synthase are confirmed and play important roles in amino acid biogenesis and metabolism. Ribose-phosphate pyrophosphokinase (EC 2.7.6.1) is a key enzyme in catalyzing the ribose-5-phosphate to phosphoribosyl pyrophosphate  [Apart from the reported loci and genes, the new allelic variation of the candidate genes e, PPRP) . PPRP ise, PPRP) ,38,39. Pe, PPRP) . Asparage, PPRP) ,42,43. IIn order to face the challenge of global climate change and meet the food demands of the ever-increasing population, QEI as a crucial role in the interaction between the gene and environment holds the potential to be exploited for the dissection of complex traits in plant GWAS studies. Among the candidate genes of the QEIs related to the three BCAA levels in rice grains, the genetic variation of the LOC_Os03g24460 (KEGG annotation: branched-chain amino acid aminotransferase in amino-acid-related enzyme pathway), LOC_Os01g55590 , and LOC_Os12g31820  genes led to the content alteration of BCAA B, which The differences in the metabolic profiles between leaf and grain in rice were reported in a comparative study . Throughr = 0.24–0.66), especially for the Ile content dataset 2012r1 (r = 0.66)  . Accordi = 0.66) . These s = 0.66) A–C, sugg . Accordindica and japonica subspecies were selected for the analyses in this study. Of this genetic panel, 272 accessions are indica rice, and 150 accessions are japonica rice. According to the geographic information of the 422 accessions, 338 accessions are from Asia, followed by 13 accessions from Europe, 12 accessions from South America, 11 accessions from North America, 5 accessions from Africa, 3 accessions from Oceania, and 40 accessions with unknown status. A randomized complete block design, containing two rows of each accession and ten plants in each row, was applied in the field-grown plants with two replicates in 2012 and 2013, respectively.A diverse panel containing 422 rice accessions of a worldwide rice collection previously released by Huazhong Agricultural University  was usedhttp://rice.uga.edu/index.shtml, accessed on 28 June 2023), clean reads were mapped to MSU 6.1 genome using the BWA software  with default settings. After the subsequent process via SAMtools software [https://gatk.broadinstitute.org/hc/en-us, accessed on 10 August 2023). A set of 3,873,686 high-quality SNPs, obtained using PLINK software  with -maf 0.05 and -geno 0.1 settings, was used for the following analyses in this study. To address the redundancy issue of a haplotype block formed by several SNPs within the same linkage disequilibrium (LD) region, a tag SNP panel including 239,055 SNPs was obtained according to the method proposed by the SR4R database [For the detection of the genetic variation, each rice accession was sequenced on the Illumina HiSeq 2000 platform to obtain approximately 1 Gb high-quality genome sequences . Using tsoftware , the SNPdatabase . This taH2 was assessed using the four content datasets of each free amino acid . The R package lme4 was used to generate the best linear unbiased prediction (BLUP) datasets for each BCAA  content [For the metabolic profiling, the randomly collected mature grains were pooled from three rice plants as described previously . In brie content .r2) between SNPs was obtained to evaluate the genome-wide LD decay [To dissect the genetic architecture of the diverse panel containing 422 rice accessions, a principal component analysis (PCA) was performed using PLINK software (v1.9) based on the tag SNP dataset obtained above. The concatenated tag SNPs for 422 accessions were used as input data for the phylogenetic analysis. A neighbor-joining (NJ) phylogenetic tree was constructed via the implementation of MEGA-CC software (v11) with the settings of pairwise gap deletion and 1000 bootstrap replicates . The outLD decay . To estiLD decay .Using the recently proposed 3VmrMLM model , the genhttps://www.genome.jp/tools/kofamkoala, accessed on 10 August 2023). The functional effect prediction of an SNP on the gene body was obtained from the RiceVarMap database  and further used for functional allele and content analysis of potential candidate genes through the Wilcoxon non-parametric test at the 5% probability level. Temporal and spatial expression profiles of candidate genes were investigated using the electronic fluorescent pictograph browser (ePlant) web tool . The relative contents of each BCAA were obtained according to the method of a comparative metabolic study [To further uncover the genes underlying the Val, Leu, and Ile levels, rice genes localized within the 122 kb (the averaged whole genome LD decay) and local LD block defined flanking regions of each detected QTN/QEI were considered as candidate genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of candidate genes were analyzed using the KofamKOALA web tool with default settings (ic study .


Despite the economic and social importance, high-yielding cassava cultivars are only released after extensive research, mainly due to the low multiplication rate. This study aimed to assess the impact of using smaller-sized seed cuttings treated with agrochemicals (8MP) compared to the conventional planting size (16 cm) on genetic parameters, agronomic performance, and the ranking of cassava clones based on yield and growth attributes. The evaluation was carried out in clonal evaluation trial (CET), preliminary yield trial (PYT), and uniform yield trials (UYT). Additionally, a new selection scheme for cassava breeding programs was proposed. A total of 169 clones were evaluated, including 154 improved clones at different stages of selection and 15 local varieties used as checks. Field trials were conducted using both sizes of propagative material (8MP and 16 cm) in each phase of the breeding program. The data were analyzed using mixed models, considering the random effects of genotype and genotype-environment interaction (G×E) to determine variances and heritabilities. Bland-Altman concordance and correlation analysis of selection indices were employed to examine the consistency in the ranking of cassava clones using different seed cutting sizes. The distribution of variance components, heritabilities, means, and range of the 8MP and 16 cm trials in different phases of the cassava breeding program exhibited remarkable similarity, thereby enabling a comparative assessment of similar genetic effects. With a selection intensity of 30%, the concordance in clone ranking was 0.41, 0.57, and 0.85 in CET, PYT, and UYT trials, respectively, when comparing the selection based on 8MP and 16 cm trials. It is worth noting that the ranking of the top 15% remained largely unchanged. Based on the findings, proposed changes in the cassava selection scheme involve increasing the number of trials starting from the CET phase, early incorporation of G×E interaction, elimination of the PYT trial, reduction of the breeding cycle from 5 to 3 years, and a decrease in the time required for variety development from 11 to 9 years. These modifications are expected to lead to cost reduction and enhance the effectiveness of cassava breeding programs. CassavaRecent advancements in technologies such as genetic transformation, genomic selection, and gene editing, offer the potential for introgression or manipulation of genomic regions to confer adaptive and agronomic advantages . However, these new approaches are more likely to succeed when integrated into breeding programs that also include conventional evaluation and adoption tests with farmers .Conventional approaches used in cassava breeding typically rely on phenotypic recurrent selection, which leads to variation in the cassava selection cycle, usually ranging from 5 to 6 years. The overall time required to develop new cultivars is around 10 to 12 years . The mai1 plant is genetically distinct. According to 1 plants from different cassava progenies offers the advantage of fully exploiting both additive and non-additive effects  interaction. G×E occurs when the contribution of alleles controlling a trait or their expression levels differ across environments . The selin vitro micropropagation, seedling production from immature leaf buds, and the more recent hydroponic semi-autotrophic (SAH) method  method . However22.1The cassava breeding program at Embrapa Mandioca e Fruticultura evaluated hybrids for the starch industry, as well as cassava varieties for fresh consumption commonly sold for the starch market during peak periods Table S1The CET utilized an augmented block design with 69 non-common treatment clones and 11 common controls, evenly distributed across 18 blocks Table 1.The cassava breeding program implemented the following trials:Two CET trials conducted in the 2022 harvest season in the city of Cruz das Almas (BA). One trial used conventional size planting material (referred to as “16cm”), while the other trial used reduced size material treated (8MP).Four PYT tests were conducted in the 2022 harvest season in the cities of Cruz das Almas (BA) and Laje (BA). One trial used the conventional 16cm size planting material, and three trials used the reduced size (8MP).Eight UYT tests were conducted in different production areas in the cities of Cruz das Almas (BA) and Laje (BA) in the 2021 and 2022 harvest seasons. Four trials used the conventional 16cm size planting material, and four trials used the reduced size material treated (8MP).In each phase of the breeding program, field trials were set up using both sizes of propagation material to evaluate the agronomic performance of the cassava clones under the same cultivation environment. This allowed for the comparison of clone rankings under the same environmental influences. This was done for the CET trials planted in the same environment (tests BR.CET.21.PP1.16cm and BR.CET.21.PP1.8MP), PYT trials (tests BR.PYT.21.SJ.16cm and BR.PYT.21.SJ.8MP), and all UYT trials in the 2021 and 2022 growing seasons Table 1.The soil preparation process followed conventional methods, starting with the desiccation of weeds and subsequently plowing and harrowing twice to incorporate crop residues into the soil. A cassava planter was then utilized to create planting furrows and apply fertilizers based on the soil analysis of each specific area. Manual planting was carried out in the furrows using stakes measuring either 8 or 16 cm, depending on the treatment being applied. The cuttings used for planting were obtained from stems that were 11 to 12 months old, ensuring they were free from pests and diseases. These cuttings were placed horizontally along the planting line. The spacing between rows was set at 0.90 m, while the spacing between individual plants was maintained at 0.80 m. Following the planting process, post-planting cultural practices adhered to the nationally recommended guidelines for cassava, as outlined by 2.2-1, mefenoxam 1.0 g ha-1, fludioxonil 1.3 g ha-1, thiabendazole 7.5 g ha-1), a binder  was incorporated to enhance the adhesion of the treatment to the surface of the seed cuttings  was calculated. This information was used to estimate the total volume of product required (TVPR) using the formula): cuttings . All pes2.3Agronomic evaluations were conducted at the harvest time of the trials, which occurred 12 months after planting. The following traits were assessed:2.3.1- measured in meters, represents the vertical growth of cassava plants.2.3.2- determined by counting the number of plants per plot and expressed as a percentage of the expected number of plants per plot. This indicates the establishment and population density of the plants.2.3.3- averaged from the evaluation of five plants in each plot, provides information about the prolificity habit and potential stem production.2.3.4- assessed on a scale of 1 to 5, indicating the overall size and branching pattern of the plants, where: 1: excellent – plants with no branches or with branches above 2.0 meters; 2: good – plants with branches above 1.60 meters or low branching, but at least 1.6 meters of erect stems; 3: medium – plants with branches over 1.20 meters or low branching, but at least 1.2 meters of erect stems; 4: poor – plants with branches over 0.80 meters or low branching, but with less than 0.80 meters of erect stems; and 5: very bad – highly branched clones with less than 0.80 meters of erect stems.2.3.5- assessed on a scale of 1 to 5, representing the coverage of leaves on the apical meristem: 1:<20% of apical meristem covered with leaves; 2: 20 - 39% of apical meristem covered with leaves; 3: 40 - 59% of apical meristem covered with leaves; 4: 60 - 79% of apical meristem covered with leaves; 5: >80% of apical meristem covered with leaves.2.3.6- evaluated on a scale of 1 to 5, indicating the thickness and strength of the stems: 1: very low vigor - stems<10 mm thick; 2: low vigor - stems between 11 and 15 mm thick; 3: medium vigor - stems between 16 and 20 mm thick; 4: high vigor - stems between 21 and 25 mm thick; 5: very high vigor - stems >25 mm thick.2.3.7- expressed as a percentage, was determined using the gravimetric method described by 2.3.8- counted in a sample of five plants per plot, provides information about root system development.2.3.9-1, represents the weight of stems, petioles, and leaves. This indicates the productivity of the above-ground biomass.assessed in t ha2.3.10-1, represents the yield of fresh cassava roots.- measured in t ha2.3.11- calculated by multiplying FRY by the DMC.2.4j that received treatment i or the control treatment i’ within block j. i (where i = i’ for the control treatment), j, and j that received control treatment i’ or regular treatment i within block j. The PYT and UYT tests were individually analyzed using a similar mixed model, but without considering the presence of a control treatment.The CET tests were subjected to a variation analysis using the following model: jth block, of the ith genotype in the kth environment. ith genotype, kth environment, jth block on the kth environment, and εjth block, of the ith genotype in the kth environment. All random effects are assumed to follow a normal and independent distribution.For the joint analysis of the PYT and UYT tests, a mixed model was employed: Heritability estimates were obtained using two approaches: i) Heritability in the broad sense , the individual analysis of the different tests  showed that genetic and residual variances were predominant. In most cases, the genetic variance was higher, except for NHP and certain UYT Figure 1Regarding the yield traits , the genetic variances in the individual analyses were greater than the variance due to the block and residual effects Figure 2Based on 3.2In the individual CET trials, the heritability of plant growth and vigor traits were generally higher in the 8MP trial compared to the conventional 16cm trial, except for the Stand trait Table 2.In the case of individual PYT tests, most of the traits showed similar median heritability compared to the CET, with slightly higher heritability values in the PYT tests, except PlArc trait Table 2.The heritability values of the individual UYT were generally comparable to those of the CET and PYT, although some variations were observed. Notably, traits such as plant height (PH), leaf retention (LeRet), and stand (Stand) exhibited higher heritability in certain field trials Table 2.Comparing the heritability between the UYT trials with 16cm and 8MP treatments planted in the same environment , minor differences were observed in the 3.3In general, the distribution of BLUP.U for trials with different treatments (16cm × 8MP) planted side by side in the same experimental area exhibited remarkable similarity across various evaluated traits. This finding aligns with the results obtained from the analysis of variance components and heritability estimation for the measured traits. On the other hand, the two additional PYT 8MP trials , conducted in environments distinct from BR.PYT.21.SJ.16cm and BR.PYT.21.SJ.8MP, displayed significant variations in agronomic performance for nearly all growth, vigor, and yield traits 4. These3.4th and 42nd positions were selected by either treatment, while clones above the last ranking were not selected by either treatment.The coincidence analysis of cassava clones aimed to compare the performance of cassava clones under different treatments, specifically standard cuttings (16cm) and reduced cuttings size with agrochemical treatment (8MP). The analysis focused on the ranking of clones based on various selection criteria, such as plant growth and vigor traits, in the CET, PYT, and UYT. In the CET tests, a correlation analysis between the selection indices (SI) of the 16cm and 8MP treatments revealed a moderate correlation (R = 0.41***), indicating some agreement in the ranking of clones. Out of the 82 clones evaluated, 13 clones (53%) were selected in both treatments, while 12 clones were selected in either one of the treatments, and the remaining clones were not selected in either treatment. The Bland-Altman analysis further confirmed the agreement between the two treatments, showing that the top-ranked clones based on SI were consistently selected in both the 16cm and 8MP treatments. Additionally, clones ranked between the 14th to the 40th positions were selected by one or more treatments, indicating some variability in clone selection between the different trials. Notably, the PYT 8MP trials selected five additional clones beyond the 40th position, which may be attributed to the evaluation of these clones in multiple environments.In the PYT, the comparison was made between the SI of the 16cm test and the BLUP.U from the joint analysis of the three 8MP trials. The correlation analysis indicated a medium magnitude correlation (R = 0.57***), suggesting a better agreement in clone selection between these two types of trials. Out of the selected clones, 12 clones (48%) were chosen in both the 16cm and 8MP trials, while 13 clones were selected in either one of the treatments, and the remaining clones were not selected. The Bland-Altman analysis supported the agreement between the two treatments, with the top-ranked clones consistently selected in both the 16cm and 8MP trials. Moreover, clones ranked from the 13th and 10th in either the UYT 16cm or UYT 8MP trials were selected, with two clones from each treatment.In the UYT trials, the cassava clones were also compared based on the BLUP.U obtained from the combined analysis of the 16cm and 8MP treatments. Unlike the CET and PYT tests, there was a strong correlation between the selection indices (SI) of these two sets of trials (R = 0.85***), indicating consistent selection patterns Figure 53.5In order to expedite the development of new cassava cultivars, the utilization of agrochemicals treated cassava seedlings with reduced size (8MP) at various stages of the breeding program, comparable to the system and commercial standard of propagation material (16 cm), can yield significant benefits. This approach holds promise for reducing the time required to develop new cassava cultivars. To capitalize on the advantages of the 8MP strategy, we propose a revised breeding scheme that incorporates its use starting from the initial clonal selection (CET) stage. By implementing this scheme, we aim to analyze the potential impact of the 8MP approach on expediting cassava development Figure 6Under the conventional cassava breeding scheme, the number of plants within a plot, the number of repetitions within a trial, and the number of environments and years of cultivation analyzed in each step depend on the quantity of propagation material generated in the preceding selective phase. This quantity is closely tied to the cassava propagation rate, which typically ranges from 1:5 to 1:10 for most cassava clones . For comWhen implementing the 8MP approach in CET, the first significant change is conducting the trials in two environments instead of just one Figure 6The farm test stage involves conducting agronomic validation tests in a minimum of 40 environments, with a minimum of 2000 plants, which is typically achieved by carrying out two evaluation cycles in the conventional system. However, with the 8MP approach, it would be possible to reduce the farm test validation period by one year. After one year following the UYT tests, more than 6000 plants of each clone would be available to set up the 40 farm tests. In the multiplication stage for farmers, another change arises as it becomes possible to work with over 35 registered multiplying agents instead of the 20 in the conventional procedure. Additionally, the 8MP approach allows for the production of over 2.9 million plants, compared to the 1.7 million in the conventional strategy. This increased production capacity significantly impacts the diffusion of new cassava varieties Table 3.The schematic layout Figure 644.1Breeding programs aim to develop superior genotypes with desirable traits that provide competitive advantages over existing varieties. The success of selecting these superior genotypes relies on the presence of genetic variability and understanding the environmental influence on trait expression. The genetic potential of breeding populations can be assessed by estimating genetic and phenotypic parameters, enabling the selection of favorable traits. Traits with higher genetic variance allow for early selection, independent of environmental effects. However, despite cassava’s wide adaptability to various environmental conditions, most varieties exhibit limited adaptability and show significant G×E interaction effects . This isFor most agronomic traits evaluated, environmental effects and G×E interactions predominated, except for traits such as PlArc and DMC, where genetic variances played a more prominent role, particularly in multi-environment trials. Previous studies have also reported a lesser influence of the environment and G×E interaction on these traits . Interes1) and self-pollinated (S1) families also found higher heritability estimates for traits like dry matter content, fresh and dry root yield in replicated trials (PYT) compared to non-replicated trials in a single environment (CET). The authors emphasized the low agreement in clone selection between the CET and PYT trials and recommended increasing the number of clones per family and using moderate selection intensity, particularly in the CET, to discard poorly performing clones in the early stages of selection.Among the genetic parameters evaluated, heritability is considered an important index as it quantifies the proportion of phenotypic variation that is attributed to genetic differences among individuals . In geneAlthough the heritability increased with improved experimental designs in the PYT and UYT trials, the differences in 4.2The progress of cassava clones in different stages of the breeding program  relies heavily on the quality of experimental trials and the accuracy of phenotyping traits under selection. This is particularly true for traits with low heritability such as fresh root yield, starch content, and disease resistance . TypicalAfter phenotyping, cassava breeders rank the genotypes using selection indices that consider economic weights assigned to various traits under analysis. This enables decision-making regarding the advancement or elimination of genotypes in subsequent selection stages. In the present study, the ranking of cassava clones using both the conventional approach (16cm) and reduced propagation material size (8MP) showed promising results. The rankings varied from moderate magnitudes in CET (R = 0.41***) and PYT (R = 0.57***) to high in UYT trials (R = 0.85***). In practical terms, this means that with a selection intensity of 30% of clones, more than half of them were selected using both approaches (16cm and 8MP) in the CET and PYT trials. In the UYT, over 80% of clones were selected using both approaches. Moreover, when employing a lower selection intensity (<30%), the agreement in the ranking of cassava clones would be even higher, as the top clones were consistently selected by both approaches.While increasing the number of trials in the early stages of cassava breeding programs has the potential to improve heritability estimates, considering the G×E interaction can complicate selection by altering genotype rankings across different growing environments. However, the modeling of phenotypic stability and identification of the most stable genotypes have become crucial in delivering successful cassava cultivars . Evaluat4.3Both the commercial production system and the conventional planting system in breeding programs rely on clonal multiplication through cassava stem cuttings, which are directly planted in the field. However, the commercial system often uses low-quality planting materials that are frequently contaminated with pests and diseases, leading to compromised crop germination, establishment, and profitability. In breeding programs, the quality of cuttings is maintained to ensure the identification of the maximum genetic potential of new genotypes. Nonetheless, both systems are limited by the propagation rate of cassava, typically ranging from 1:5 to 1:10, depending on the variety . ConsequIn vitro micropropagation is one such technique, capable of producing high-quality planting material on a large scale and within a short period  for the subsequent multiplication step, intended for distribution to end-users. The introduction of the 8MP approach and the modification of the breeding scheme proposed in our study represent important advancements in cassava multiplication and improvement. These changes have the potential to enhance the evaluation of clones in various environments at an earlier stage, expedite the breeding cycle, and increase the availability of high-quality planting material for farmers. By integrating these innovations, we can accelerate the development of improved cassava varieties, ultimately benefiting both breeders and end-users.In recent years, cassava breeders have shown increased interest in exploring alternative selection methods. While the focus of our results presentation has primarily been on the ranking and behavior of clones as they progress through the breeding program, the proposed changes would introduce important shifts in cassava population improvement. Traditionally, improvement has been based on clone performance rather than individual genetic values. Only recently have changes been made to this paradigm through the implementation of genomic selection in various cassava breeding programs . Genomic4.4The urgency of addressing climate change and its impact on global food production necessitates the development of more sustainable solutions. Cassava, being a resilient and versatile crop, holds great potential as a food, feed, and industrial crop in the face of predicted adverse climate scenarios. However, the genetic improvement of cassava has been slower compared to other crops like cereals, primarily due to its long reproductive cycle and low multiplication rate. Therefore, there is a need to innovate traditional breeding schemes to expedite the availability of improved varieties that exhibit resistance to diseases and stresses associated with rising temperatures and water scarcity, while also meeting the quality requirements of end-users.In this study, we propose a novel breeding scheme that enables the early implementation of multi-environment tests in breeding programs using smaller seed cuttings treated with a formulation containing protective agrochemicals and growth stimulants. This approach allows for the estimation of genetic parameters and the ranking of cassava clones in a manner similar to conventional selection schemes. However, the significant advantage of our approach is the incorporation G×E interaction evaluation at the CET, eliminating the need for separate multiplication and selection steps. Furthermore, it increases the availability of propagation material for cultivars to be distributed to end-users in the final stages of the breeding program and reduces the time required to develop new cultivars from 11 to 9 years. This reduction in time also translates to cost savings in genetic programs.By implementing this new scheme, we aim to accelerate the genetic improvement of cassava, enabling the development of more productive varieties with enhanced resistance to diseases and stresses associated with climate change. These innovations will contribute to ensuring food security and meeting the demands of end-users while addressing the challenges posed by a changing climate.The original contributions presented in the study are included in the article/LC: Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. DC: Investigation, Methodology, Software, Supervision, Validation, Writing – review & editing. DK: Conceptualization, Project administration, Resources, Writing – review & editing. MR: Conceptualization, Resources, Supervision, Writing – review & editing. EO: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing.


Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.Upland cotton ( It encompasses approximately 44 diploid (2n = 2x = 26) and seven tetraploid (2n = 4x = 52) species  constitutes ~95% of the global cotton production because of its high adaptability to various environments and significant yield (https://usda.library.cornell.edu/). In India, most cotton cultivars are released using conventional breeding techniques based on morphological traits that are affected by agronomic practices and environmental changes. Consequently, the diversity and quality of elite cotton are reduced daily because of the narrow genetic background, domestication, and selection of elite cotton cultivars , boll number (BN), seed cotton weight (SCW), lint percentage (LP), lint index (LI), seed index (SI), first fruit branch position (FFBP), plant height (PH), flowering period (FP), fruit spur branch number (FSBN), and many others . Such quG. arboretum . It determines whether particular alleles in a population are more frequently associated with specific phenotypes than expected, thereby providing insights into the genetic basis of complex traits . In addirboretum . Subsequrboretum , seed oirboretum , fiber yrboretum , and biorboretum , as wellrboretum .To date, studies on association mapping using Indian germplasm are scarce and were first reported by Therefore, the specific goals of the present study were to (a) examine the phenotypic variability within the upland cotton germplasm released from different cotton growing belts in India; (b) explore the QTNs  or SNP markers underlying fiber yield traits; (c) compare identified QTLs with previously reported QTLs for yield traits, if available; and (d) identify and validate through expressional profiling of putative candidate genes found within the genomic regions controlling yield traits. Our results using 117 diverse Indian germplasms and 2,41,086 SNPs identified many stable and novel QTL/genes that may offer crucial information on the genetic control of fiber yield traits in cotton. This information will aid in improving cotton yield and the development of elite Indian cotton varieties through marker-assisted breeding programs.22.1G. hirsutum L.) germplasm was procured from All India Coordinated Research Project (AICRP) on Cotton, ICAR-Central Institute for Cotton Research, Regional Station, Coimbatore, India. These materials were selected based on their phenotypic expression with reference to fiber yield. The germplasms used in this study constitute the varieties released from diverse cotton-growing belts  of India. Out these, 31, 43, and 32 germplasms were from the northern, southern, and central zones, respectively, and information was unavailable for 11 germplasms zones  and in the southern zone, it was grown at Tamil Nadu Agriculture University, Coimbatore . There was a significant disparity in the agro-climatic conditions between the two cotton-growing regions, including variations in the soil type, rainfall, temperature, and growing season. Cotton cultivation in E1 was done in alluvial soil during May–November 2021 (under high temperatures), while in E2, plant materials were grown under red soil at relatively lower temperatures.2.3The plants comprising the association panels were sown following a randomized block design (RBD) with ten biological replicates of each germplasm accession. The plant-to-plant spacing was 45 cm, whereas the row-to-row spacing was 90 cm, and there were 10 plants in each row.The cotton crop in E1 was established following regular practices such as field preparation with fine tilth, sowing with a single seed/hill, fertilizer applications . Imidacloprid was applied to control mites and sucking pest infestation that was noticed during the early period of the cropping program (25th DAS). In E2, cotton cultivation was supplemented with 12 kg/acre of phosphorus as a pre-planting application, followed by separate applications of 15 kg/acre of nitrogen during both the thinning and flowering stages. Osheen 20 SG (dinotefuran) was applied to safeguard the cotton crop against sucking pests such as whiteflies and jassids to maintain healthy crops.At maturity, all opened bolls were harvested from the surviving healthy individual plants to estimate the fiber yield traits, such as BN, BW(g), LI (g), LP (%), SCY(g/plant), and SI (g). For BN, the total number of bolls was counted from each plant, BW was measured as the average weight of 10 mature healthy bolls from each plant, and SCY per plant was the total weight of the seed along with the lint. Other traits were subsequently measured after ginning the cotton bolls; LP (Ginning Out Turn) is defined as the percentage of lint weight obtained from a given weight of seed cotton and was calculated using the formula given below; LI is the weight of the lint produced by the 100 seeds and it was calculated using the formula given below; SI was measured as the weight of 100 healthy seeds.2.42), and BLUP-based prediction of the mean for the multi-environment trait (two locations) were calculated using the “METAN,” “Phenotype,” and “corrplot” packages in R environment , standard deviation (SD), Pearson’s linear correlation coefficients, variance components, Broad sense heritability (hironment . GWAS an2.5silico optimized restriction enzyme set MseI (frequent cutter) and HaeIII_MspI (rare cutter) followed by efficient adapter ligation. After library preparation, high-throughput paired-end DNA sequencing was performed using the Illumina® HiSeq 2500 platform. Variant calling was performed using the Genome Analysis Toolkit  (G. hirsutum TM-1 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_007990345.1/) using BWA-mem, and then a clean BAM file was created using MergeBamAlignment (Genomic DNA was extracted from fresh young leaves using a modified cetyltrimethylammonium bromide (CTAB) method . To ensu4.2.6.1) . In brielignment . Clean Blignment .2.6To estimate the genetic differences between these 117 Indian cotton germplasms, population structure, molecular phylogenetic (neighbor-joining), principal component analysis (PCA), and kinship (k) analysis were performed. The filtered variants were pruned based on linkage disequilibrium (indep 50 5 0.5) using Plink (version 1.9) for PCA and population structure analysis . The pru2.7The Genome Association and Prediction Integrated Tool (GAPIT), an R package was used for SNP (marker) trait association analysis. Three single-locus models , and three multi-locus models  were used . The kin2.8http://cotton.zju.edu.cn/Qtl_phe.html database and association mapping reports; (2) the physical location/genomic coordinates of the SNPs in the QTL loci were retrieved through BLASTn (250 Bp flanks) against the G. hirsutum genome m genome , where hm genome .2.9RNA-Seq datasets obtained from various cotton tissues, including root, stem, leaf, torus, seed, cotyledon, ovule, fiber at 5 days post-anthesis (DPA), fiber at 10 DPA, fiber at 20 DPA, and fiber at 25 DPA, were downloaded from NCBI BioProject with the accession number PRJNA248163.The data were preprocessed for quality and adapter trimming using the Trimmomatic tool in the PE mode . Alignme33.1The present study evaluated the phenotypic variability of six yield-related traits for an association panel of 117 Indian upland cotton germplasms in two environments, E1 and E2. A significant and extensive range of phenotypic variation was observed for all investigated traits Table 1.2) ranged from 6.32% to 24.7% and 63.3% to 90.6%, respectively for E1, whereas in E2 the same was ranging from 6.17% to 20.1% and 46.6% to 78.3%, respectively for all six yield-related traits (The coefficient of variance (CV) and heritability (hd traits Table 1.The effects of genotype (G), environment (E), and genotype-environment interaction (G × E) for all six traits were assessed using analysis of variance (ANOVA). Significant variation (P<0.001) was observed, indicating that adequate variability was present for all six quantitative traits and was controlled by G, E, and G × E effects  using Illumina® HiSeq 2500 platform. Details of the sequence read statistics are provided in The SNP call performed with the GATK pipeline resulted in a total of 14,46,969 SNPs, of which 2,41,086 high-quality SNPs were retained after filtering , which was used further for genetic variation and GWAS analysis. SNPs had transitions (A/G or C/T) of 5,549,405 bp and transversions  of 2,339,098 bp, with a Ts/Tv ratio of 2.37%. The SNP distribution across the genome was not evenly distributed, depending on the genome content and gene density, with 1,47,347 and 93,739 SNPs in the At and Dt sub-genomes, respectively. The number of markers varied among the chromosomes, with a maximum number of SNPs  in Chr A08 and a minimum number of SNPs  found in Chr D03. The average SNP density throughout the genome was approximately one SNP per 11.36 kb , neighbor-joining (NJ) phylogenetic analysis, and kinship coefficient analysis. Understanding population structure is crucial in genome-wide association studies (GWAS) because the presence of a structure can influence the reliability of the association found. The LnP(K) value continuously increased from K = 1 to 10  value of 4.99, and NC_053441.1_63281905 had a negative effect (−3.606) with a −log10(P) value of 5.08. For BW, 27 significant SNPs were located on chromosomes A06, A08, A09, A10, A11 A12, A13, D04, D06, D07, D09, D10, D11, and D12, with the maximum number in A11 (five SNPs). Of these SNPs, NC_053432.1_54551322 showed a positive phenotypic effect (0.430) with a −log10(P) value of 5.49, and NC_053433.1_116352175 showed a negative effect (−0.465) with a −log10(P) value of 7.36. For LI, 50 significant SNPs were detected on chromosomes A01, A03, A04, A05, A06, A07, A11, A13, D01, D04, D06, D08, D09, D10, and D12 with six SNPs in A06. The phenotypic effect size of these SNPs ranged from 0.480 (NC_053429.1_20687114) to −0.47 (NC_053442.1_33992120) with −log10(P) values of 5.75 to 5.47, respectively. For LP, 40 significant markers were observed in 14 chromosomes of the cotton genome, with the maximum number in Chr D10 (nine SNPs). They explained the −2.072 (NC_053444.1_64810245) to 2.285 (NC_053441.1_58365634) range of phenotypic effect size having a P-value of 5.1 to 5.89. A total of 28 significant markers were observed for SCY located on 12 chromosomes, with the highest occurrence at D06 (nine SNPs), contributing 16.696 to −15.449 phenotypic effect size. Loci NC_053427.1_2009750 and NC_053431.1_72867934 had positive and negative effects on SCY, respectively, with −log10(P) values of 6.66 to 4.53, respectively. Similarly, SI had a total of 32 significant markers distributed on chromosomes A02, A05 (six SNPs), A07, A08, A10, A13, D01, D02, D07, D08, D09, and D12, explaining −0.876 to 0.959. The −log10(P) values of the loci contributing to positive effects (NC_053428.1_79218940) and adverse effects (NC_053431.1_70272704) were 5.45 to 4.99, respectively.To identify the most suitable model for conducting GWAS analysis of our datasets , six statistical models  were compared using a quantile–quantile (Q–Q) plot (3.5qGhBN_D11-1) had seven significant markers. All QTLs and GWAS signals for yield-related traits were retrieved from the database to compare our QTLs with those in previous reports. A total of 535 QTLs were reported for yield-related traits in 33 r QTL mapping studies. Similarly, details of the significant markers and their genomic positions were extracted from six reports of association mapping studies. Among the 134 QTLs identified in these studies, 39 were novel and newly identified in the Indian germplasm, and the remaining 95 QTLs overlapped with previously reported QTLs  on chromosome D06, were found to be associated with BN and SCY having common significant markers  within the same genomic intervals (35.8–38.1 Mb). Also, three QTLs  exhibited pleiotropic association for SI and LI, of these qGhLI_SI_A13 had a common marker (NC_053436.1_78553469) for LI and SI while the other two had overlapping QTL intervals in terms of physical position (25.54–25.94 Mb in A05 and 5.67–6.07 Mb in D09). Moreover, BW and SCY had one pleiotropic QTL (qGhBW_SCY_A10) because of the presence of a common SNP marker (NC_053433.1_116352175) within genomic intervals of 116.15–116.55 Mb. In addition, one QTL (qGhLP_BN_A8) showed an association between LP and BN owing to the overlapping of QTL intervals in Chr A08 (59.74–60.14 Mb). As these traits showed a significant correlation at the phenotypic level , they were also found to have pleiotropic-associated markers at the genomic level. This result implies that a network of QTLs with multiple phenotypic effects may control fiber yield traits.According to the definition of QTLs from the previous methods , a total3.6qGhBN_SCY_D6-2, associated with BN and SCY traits on Chromosome D06, formed a haplotype block with five SNP markers, which consisted of six haplogroups in our association panel  QTLs had<5 putative genes, 33 QTLs (~24%) had >5–<10 putative genes, while all the remaining 85 QTLs (~63%) covered >10 genes , GhGELP (highest expression at 20 DPA fiber development stage), and GhCBSX5 (highly active in stamen tissue and at the 10 DPA of fiber development stage). For the BW trait, qGhBW_D9-3 had two highly expressed genes (GhAGX2 and GhEP1-3) showing higher expression at 10 and 20 DPA at the fiber development stage, respectively. In addition, the BW QTL qGhBW_A11-1, qGhBW_D9-1, and qGhBW_D9-2 contained the highly expressed gene GhSMP1 , GhGRP4 , and GhCSGL3 (positive activity in seeds at 5 and 10 h), respectively. The qGhLI_A4-2 QTL for LI has two genes, GhbZIP11 (showing a higher expression profile in ovule and fiber development stages) and GhLEA (showing higher expression in ovule development). Other LI QTLS, qGhLI_A6-1, qGhLI_D6-2, qGhLI_D9-1, and qGhLI_D9-2 show expression profiles for GhKNAP2 (expressed in root tissue), GhCYP , GhMIF2 , and GhGA20OX1 (highest expression 20 DPA of fiber development) genes. Seven LP QTLs, qGhLP_A5-1, qGhLP_A6-1, qGhLP_A10-1, qGhLP_A10-3, qGhLP_A10-6, qGhLP_D8-2, and qGhLP_D10-3 contained GhPDF1 , GhMADS23 , GhGUX1 (expression at 20 DPA of fiber stage), GhBBE18 (expression at 25 DPA of fiber stage), GhLEA-D-19 (high expression at 0 and 5 h of seed development and 35 DPA of ovule development), GhchlADH1, and GhABCC . The qGhSCY_A4-1 QTL of SCY contains seven genes  and showed interesting expression profiles that were highly expressed in stamen tissues in contrast to other tissues, especially the pistil. In addition, qGhSCY_D2-1 for the SCY trait had two expressed genes (GhPUP4 and GhDCTPP1) that were highly expressed in ovules at all stages (except at 35 DPA). The other two SCY QTLs (qGhSCY_A8-2 and qGhSCY_D2-2) showed a high expression profile for GhAP2/ERF_AIL5  and GhGGAT2 (expressed 25 and 35 DPA of fiber development), respectively. For SI, qGhSI_D2-3 contains two genes, GhAGP9  and GhZAT10 (activity at 25 DPA of both ovule and fiber development). Similarly, the qGhSI_D8-2 QTL for SI had two active genes, GhACO3 and GhFLA7 (which showed the highest activity during the ovule and fiber development stages). In addition, qGhSI_D12-1 contained GhMYB22, which showed higher expression at an early stage of ovule development (−1 to 1 DPA). In addition, of the eight pleiotropic QTLs, two QTL qGhBN_SCY_D6-3 and qGhSI_LI_A5, governed three active genes. qGhBN_SCY_D6-3 had two active genes, GhPPR , and GhCHUP1 (highest activity at 5 and 10 DPA of fiber development and 20 and 25 DPA of ovule development) while qGhSI_LI_A5 had GhSCPL42 . Of the 870 active genes, 40 candidate genes were selected for six yield-related traits that showed more contrasting expression profiles in the ovule, fiber, and seed developmental stages in comparison to other cotton tissues . Of the 2,509 candidate genes, 870 were found to be transcriptionally active, showing a zFPKM value of ±3 in at least one tissue , and all six traits exhibited abundant phenotypic variation ranging from 6.32% to 24.7% and 6.17% to 20.1% in E1 and E2, respectively. Our findings also showed the presence of medium to high heritability , illustrating the importance of these germplasm lines for genetic improvement and showing the same phenotypic expression if sown in the same environment , while the other two were overlapping QTLs. Similarly, qGhBW_SCY_A10 and qGhLP_BN_A8 were pleiotropic QTLs for BW/SCY and LP/BN, respectively. Additionally, correlation analysis of these yield-related traits showed a significant positive correlation , and the maximum negative correlation between LP and SI indicated that these are the favorable loci that could be the genetic foundation for the correlation between these traits. Thus, the pleiotropic loci identified in this study provide information to explore the molecular mechanism that explains the simultaneous enhancement of six intricate yield-related traits in cotton, which will be helpful in the selection of germplasms in crop improvement programs for yield traits in Indian upland cotton.Several studies have indicated that effective control of the discovery of false positives resulting from population structure in crops may not be entirely achieved . TherefoqGhBN_D5-1, qGhBN_D6-1, qGhBN_D6-2, qGhBN_D6-3, and qGhBN_D6-4) for BN overlap within TM58714_TM58742_TM58749  showed consistent results with TM47610_TM47614, TM56685_TM56686, TM63749  corresponds to QTLs qLI-D6-1  matched with the LP GWAS signals reported by A06_102555770), and QTL reported by F2:3-qLP-c25-1), and qLP-D8-1/qLP-F2:3-JES-1a), respectively. Four QTLs  were associated with SCY in the present and in the previously reported SCY QTLs: qSCY-07A-c13-1, qSCY-06A-c25-1  showed congruency for both BN (qBPP-1-43.5) and SCY (qSCY-06A-c25-1) traits, as reported previously (qGhBW_SCY_A10 and qGhLI_SI_D9) were consistent with the BW association signal A10_99131954  were functionally validated to play a major role in the cotton fiber development process (GhLEA and GhbZIP11) have been reported to be stress-responsive in cotton  have direct roles in cotton seed germination, fiber cell initiation, and elongation  were newly identified genes and might play a vital role during fiber development based on their expression profile; however, the functions of these in cotton remain to be further explored. Interestingly, for SCY highest number of genes was found (eleven) out of five GhPG, one GhPL, and one lncRNA reported encodes the highly significant marker (NC_053427.1_2009750) showing the highest phenotypic effects (~16.69%) could be the most promising genes found in the present study that could play a major role in fiber yield trait, which also corroborates with previously reported studies , which play an active role in fiber initiation elongation and freezing tolerance, as reported previously (qGhBN_SCY_D6-3 and qGhSI_LI_A5) have three active genes , whose functions in cotton fiber development have not been deciphered; however, their isoforms have been reported to play a functional role in defense mechanisms against abiotic and biotic stress  can be found below: NCBI SRA accession number: PRJNA982386.The manuscript number allotted by the ethical committee is CSIR-NBRI_MS/2023/06/10.SNJ and SVS conceptualized the idea of the project. NMB and HK performed field cultivation of cotton germplasms at TNAU, Coimbatore, Tamil Nadu, and PAU Faridkot, Punjab, respectively. BJ conducted the phenotyping and performed all the experiments, whereas GJT participated in phenotyping only. SS, BJ, SJ, and MI conducted all the computational and statistical data analyses. BJ wrote the first draft of the manuscript and SS helped into it. GJT, NMB, DA, SJ, DK, SVS, and SNJ revised the manuscript and provided various suggestions. BJ includes all the corrections and suggestions in the final manuscript. All authors contributed to the article and approved the submitted version.