Senescence is a programmed process that involves the destruction of the photosynthesis apparatus and the relocation of nutrients to the grain. Identifying senescence-associated genes is essential to adapting varieties for the duration of the cultivation cycle. A genome-wide association study (GWAS) was performed using 400 inbred maize lines with 156,164 SNPs to study the genetic architecture of senescence-related traits and their relationship with agronomic traits. We estimated the timing of senescence to be 45 days after anthesis in the whole plant and specifically in the husks. A list of genes identified in a previous RNAseq experiment as involved in senescence (core senescence genes) was used to propose candidate genes in the vicinity of the significant SNPs. Forty-six QTLs of moderate to high effect were found for senescence traits, including specific QTLs for husk senescence. The allele that delayed senescence primarily increased grain yield and moisture. Seven and one significant SNPs were found in the coding and promoter regions of eight core senescence genes, respectively. These genes could be potential candidates for generating a new variation by genome editing for functional analysis and breeding purposes, particularly Zm00001d014796, which could be responsible for a QTL of senescence found in multiple studies. Agricultural residues are an interesting and attractive bioenergy feedstock since they do not rival food, and their exploitation does not entail arable land loss. In the case of cereals, dual-purpose varieties can be exploited using the grain for feed or food, and the stalks and cobs for bioenergy. More experimental information about the genetic basis of traits related to the exploitation of residuals and the correlations between residual and grain characteristics is needed to design an efficient breeding strategy for developing dual-purpose varieties.The two challenges facing the world today are food and energy. In the present situation, the most substantial proportion of energy originates from non-renewable energy sources, which are not sustainable and are harmful to the environment because of the high CO warming . UtiliziZea mays L.) is an essential annual cereal crop for human food, animal feed, and industrial use. It has a C4 photosynthesis mechanism that is very efficient and generates a large amount of biomass. The life cycle of maize is classified into vegetative phases (sowing to flowering), grain filling, senescence, and death. Senescence is a complex activity that involves the degradation of the photosynthetic apparatus and many activities, such as the breakdown of macromolecules, accumulation, redistribution, and remobilization of nutrients, such as nitrogen [Maize  as the most significant cluster of subpopulations (ΔK) a. The prA summary of mean phenotypic traits and individual year data is shown in Most of the lines with a VSP equal to 1 had low grain moisture and low yield . In contp > 0.05)  . Regardi2 m−2 s−1, and for SC, they varied from 0.01 to 0.04 mol H2O m−2 s−1. The proportion of phenotypic variance (R2) varied between 4 and 8% across all the traits. The number of marker trait associations (MTAs) was ten or close to ten for all senescence traits , except for VSH, which doubled that number (22 MTAs). Eight markers were significant for more than one variable. Marker S6_163349566 on chromosome six was significant for visual and quantitative variables . The sign of the allele effect was the same for the three traits; that is, the allele that increased VSP also increased VSH and PA. S1_209120152 was also associated with a visual scale of the husk (VSH) and a quantitative variable (PA). Again, the sign of the allele effect was coincident. S7_139201593 (C/T), S5_214537055 (A/G), S5_167009258 (C/T), and S3_5884845 (C/T) were significant for PA and SC. S5_147166110 (C/T) and S5_61802239 (C/T) were significant for the two visual traits, VSP and VSH. Although S6_162646831 is the only SNP significantly associated with senescence (VSP) p = 3.76 × 10−5, allelic effect = 0.67, and an agronomic trait (RDW) p = 3.40 × 10−5, allelic effect = 13.83), among all forty-six combinations of the senescence traits and the agronomic traits, the sign of the allelic effect was the same, except in three cases   distributed in all chromosomes . The all7840430) . The allWe found sixty-one genes (two genes are repeated) in the window of ±200 kb of the significant SNPs . Among tThe mean of 500 iterations of the genomic selection accuracies for the traits based on visual scales, VSP and VSH, and PHVDIF, were 0.24, 0.20, and 0.15, respectively, while the accuracies for the traits measured with the IRGA, PA, and SC were 0.14 and 0.08. The accuracies of the iterations followed a normal distribution (boxplots are shown in 2 interchange per leaf area (PA trait) [Senescence in annual crops, such as maize, involves changes in the expression of thousands of genes and the activation and inactivation of multiple metabolic pathways . At the A trait) , and agaA previous study observed that the visual color rating was correlated with chlorophyll content per unit area and that chlorophyll content was nearly correlated with photosynthesis activity . SimilarAlthough agronomical practices, such as row direction and plant spacing, have been studied for husk senescence , there iThe expected effect of extending photosynthesis activity is grain yield increment, and we congruently found a high positive correlation between senescence traits and grain weight. However, we also identified a high genetic correlation between senescence and grain moisture; the delaying of senescence has a detrimental effect on grain moisture, which is prejudicial for grain storage . The optWe expected, a priori, a substantial effect of husk senescence on agronomic traits, particularly grain moisture, because the husks embrace the ears . HoweverRegarding cultivating varieties for dual purposes , we found that senescence traits are highly correlated to both the yield and the moisture of the residuals. The senescence affects the yield of grains and residuals in the same direction; therefore, there are no complications in improving both traits simultaneously. However, the adequate moisture of residuals depends on bioenergetic use ,31. For 2 interchange or the visual scale. A high power to detect QTLs for senescence is expected due to its high heritability [We did not find a large number of QTLs for plant senescence measured with either the COtability . Moreovetability ,34,35 antability ,34,35. Ttability ,34,35. Ttability ,34,35,36We detected QTLs related to husk senescence for the first time, and some of them did not colocalize with QTLs for whole plant senescence, suggesting that they can be specific to husk senescence. This is consistent with a substantial part of the genetic variation of VSH not explained by its relation to VSP. For some of the significant SNPs, the allelic effects were high, for example, S1_6315814  or S3_217840430 . Although VSH had lower heritability than VSP, the number of QTLs detected for VSH doubled the number detected for VSP, suggesting a more complex genetic architecture. We also detected QTLs for the difference between the senescence of the whole plant and husks (PHVDIF), some of them with a relatively high allelic effect, for example, S3_148031626 . The QTLs for VSH or PHVDIF with a relatively significant effect could be useful in breeding for modifying the traits or investigating the genetic basis of senescence in husks.We expect that several QTLs for senescence traits colocalize with QTLs for agronomic traits because the genetic correlation between the two types of traits is high. This was not the case, and only in one region were QTLs for the two types of traits localized together. However, out of 46 senescence QTLs, 43 had alleles with signs consistent with agronomic traits. The allele that had later senescence also had a higher yield and moisture, although the difference among the alleles for agronomic traits was not significant. The high consistency of the signs suggests a true association among several QTLs for both kinds of traits. However, we cannot have high confidence in any specific association, except one, because of the low probability associated with agronomic QTLs. The relatively large size of our mapping population has two opposite effects on the estimation of the QTL effects and years: an increment in precision due to the higher number of recombinants but a decrement in precision due to a higher experimental error. The experimental error could also explain the lack of strong significance of the senescence QTLs, despite the relatively large effects. The larger sizes of the field experiments limit the rise in precision achieved by increasing the number of genotypes in the association panels. Nowadays, when the limitation of insufficient genome coverage with molecular markers seems to be overcome, increasing the precision of phenotyping in field experiments is an active area of research ,38.The accuracy of genomic selection was higher for VSP than for PA, which was expected given its high heritability. The magnitude of the accuracy of genomic selection for VSP was in the range of traits, such as grain yield in maize . TherefoEight significant SNPs were located in the coding or promoter regions of core senescence genes identified in a previous RNAseq experiment ,20. We pArabidopsis thaliana shows a high ammonium content with a chlorotic phenotype [Nicotiana tabacum during senescence, GS2 is a pivotal enzyme that relocates the assimilation of ammonia from the chloroplast to the cytosol of mesophyll cells (MC) [On the other hand, a knockout mutation of GS2 in henotype . In Nicolls (MC) . An expells (MC) . We alsolls (MC) ,20.A. thaliana and is described as an EXCYST subunit or EXO70G1. It belongs to a gene family involved in diverse land plant functions: protein recycling, cytokinesis, biotic stress interactions, defense, secondary cell wall pathways, and many more [A. thaliana have fewer anthocyanins, which are essential components that safeguard the photosynthetic apparatus during senescence [Zm00001d014642 is orthologous to AT4G31540 of any more . The EXOany more ,48. Addiany more . Mutationescence .Of the eight significant SNPs located in the core senescence genes, only S6_162646831 was in the promoter region of Zm00001d038911. S6_162646831 was the only SNP significant for senescence and agronomic traits (VSP and RDW). This gene codifies a non-specific lipid transfer protein (nsLTP). The gene has an 85% similarity with its orthologue in sorghum, indicating a high level of conservation among both species. LTPs are small, essential proteins available in high amounts in higher plants and are responsible for the inter-transportation of phospholipids, glycolipids, fatty acids, and many other amphiphilic compounds between membranes in in vitro conditions ,51,52. AA. thaliana, APRT is a crucial metabolic enzyme in the inactivation of cytokinins, essential hormones that regulate the senescence process [A. thaliana and is involved in histone deacetylation and condensation of heterochromatin. Zm00001d016802, which encodes ascorbate peroxidase (APX). APX is an enzymatic antioxidant that prevents the uncontrolled oxidation of cellular compounds by scavenging H2O2, one of the ROS in plants produced during photosynthesis and photorespiration [The candidate gene Zm00001d017204 codifies an adenine phosphoribosyltransferase (APRT) enzyme that removes adenine and adenosine, which are considered toxic within chloroplasts . In A. t process . Zm00001 process . This ge process ,34,35,36 process , which ipiration ,59. Howepiration ,20, but 2, with a density of approximately 7 plants/m2. Each experimental plot consisted of 14 plants.We evaluated 400 inbred lines of temperate, tropical/subtropical, and mixed origin, including representatives of the important groups used in breeding within temperate germplasm, such as Stiff Stalk, Lancaster, Iodent, and European Flint . The gerhttp://www.panzea.org (accessed on 15 November 2018)) [www.maizegdb.org (accessed on 20 of December 2018)) [The genotypic data of the inbred lines used for association analysis were provided by Cornell University and consisted of 1,000,000 SNPs and are available on a public database, Panzea (r 2018)) ,61. Heter 2018)) . The phyr 2018)) ,64.The genetic structure of the population was estimated using the Bayesian clustering algorithm implemented in the STRUCTURE software v2.3.4  using an2 assimilation rate (mol CO2 m−2 s−1), which indicates photosynthesis activity (PA), was measured with an infrared gas analyzer (IRGA) portable photosynthesis system  [2O m−2s−1) was recorded with the portable photosynthesis system. After 45 DAA, the visual senescence of the plant (VSP) and the visual senescence of husks (VSH) were estimated on a visual scale from 1 to 5, where one corresponds to dried leaves and five to whole green leaves or husks. The visual scales were evaluated based on all the plants in the plots. We calculated the difference between VSP and VSH (PHVDIF) to assess the degree of association between the senescence of husks and the remaining parts of the plant.The net CONE, USA) . MeasureNE, USA) . We chosNE, USA) . In thosA digital moisture meter calculated the grain moisture (GM) (g/Kg). The ears were removed from the vegetative fraction, and the grain dry weight (GDW) (g/plant) was estimated by fresh weight after adjustment by GM. Residual dry weight (RDW) (g/plant), including leaves and stalks, was measured after placing the fresh samples in a stove for five days at 60 °C. The residual moisture (RM) (g/Kg) was calculated by subtracting the proportion of dry weight from the freshly harvested plant weight.r = 1).Analyses of variance and box and normal distribution plots were calculated for each individual year and for the combination of the three years using PROC MIXED and PROC UNIVARIATE procedures of SAS ,70. The y is the vector of BLUEs, β is a vector of SNP marker fixed-effects parameters, u is a vector of random additive effects of inbred lines, X and Z represent matrix, and e is a vector of random residuals. The random inbred line effect was estimated as Var(u) = K n × n matrix of the pairwise kinship coefficient and −5 as the threshold because the point where the observed and expected F-test statistics deviated in the Q-Q plot was equal to or lower than this value for all traits [https://github.com/YinLiLin/CMplot (accessed on 6 December 2021)). A QQ-plot was generated in TASSEL V5.2.25 [BLUEs (best linear unbiased estimates) of genotypes were calculated using PROC MIXED. Years, the interaction of the years with genotypes, and blocks within years were considered random effects. We performed the GWAS in TASSEL V5.2.25 , followivariance . Restricl traits ,74,75. Tl traits . Circula V5.2.25 .https://www.ncbi.nlm.nih.gov/bioproject/ (accessed on 16 May 2022), PRJNA746402 [https://www.maizegdb.org (accessed on 20 December 2018)) [https://www.uniprot.org (accessed on 2 September 2019)) annotation hub database [We chose the genes within a window of ±200 kb of SNPs significantly associated with a senescence trait. Within those, we exclusively ,20. In tr 2018)) ,64, unipdatabase , and a lRidge regression best linear unbiased prediction (RR-BLUP) ,79 was uIn this study, we presented the results of the evaluation of the largest association panel examined thus far in order to study the genetics of senescence, which was measured directly as photosynthesis decay for the first time in a mapping experiment in maize. The high genetic correlations  and the high coincidence of the allelic effects (in 93% of the significant SNPs) among senescence and agronomic traits clearly showed the importance of senescence in adapting the crop cycle to the duration of the cultivation season. Eight of the significant SNPs detected in the association analysis were found in the coding or promoter regions of genes significantly associated with senescence in an independent RNAseq experiment. These genes are strong candidates for subsequent functional analyses to widen our knowledge of the genetics of plant senescence and for use in breeding to optimize the timing of senescence in different environments.


The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.  •Expression prediction and gene-trait associations do not port across ancestry groups•Recommend meta-analysis across both ancestry and study for least test statistic inflation•Detail mechanistic tests to contextualize results from TWASs Bhattacharya and Hirbo et al. describe a framework to identify gene-level genetic associations with complex traits through meta-analysis of multi-ancestry datasets from biobanks using transcriptome-wide association studies (TWASs). They demonstrate pitfalls in TWASs and review open methodological challenges, including lack of portability of expression prediction and tissue specificity of results. We explore practical considerations for all three steps of the TWAS framework  and S1: We outline the TWAS framework employed in analyses using GWAS summary statistics from the GBMI .3 First,GBMI’s diversity enables uniquely well-powered studies to detect trait associations in non-European populations. However, optimal TWASs require well-powered training datasets of genetic and tissue-specific gene expression data, which are still lacking for non-European populations. Power to detect GTAs in TWASs is dependent on expression heritability and the predictive power of the expression model.,2 to account for sample size (In five tissues in GTEx with >70 samples from both European (EUR) and African (AFR) ancestry, we trained EUR- and AFR-specific models using elastic net regularized regression and imputed expression into the aligned  and misaligned  groups.ple size . For thi2) in AFR samples compared with models trained in AFR samples  models imputed into the same sample  of predictive SNPs between EUR and AFR ancestry populations. Importantly, this discrepancy is not generally specific to any one ancestry. Rather, ancestry imbalance in training or reference datasets may lead to poor portability of genetic models because of MAF differences. To incorporate common SNPs in both AFR and EUR ancestry populations, we trained ancestry-unaware and ancestry-specific models using SNPs with MAF exceeding various thresholds in both AFR and EUR samples. Excluding SNPs with MAF <0.01 improved predictive performance of ancestry-unaware models across all tissues . HoweverAnother critical consideration for GBMI involves meta-analysis with GWAS summary statistics. TWASs estimate a GTA by weighting the standardized SNP-trait effect sizes from GWAS summary statistics by SNP-gene weights from the expression models. To account for the correlation between SNPs, an external LD reference panel, like the 1000Genomes Project,Z scores across EUR and AFR ancestry groups were not strongly correlated . The GWAS effect sizes of SNPs local to DFFA do not show large deviations in effect direction and are only nominally significant as well  inverse-variance weighting (IVW) and (2) effective sample size weighting (SSW), meta-analyzing across ancestry-specific meta-analyzed GWAS summary statistics using (3) IVW and (4) SSW, and (5) performing a TWAS using ancestry-unaware models and meta-analyzed GWAS summary statistics across EUR and AFR ancestry groups . QQ-plot cohorts .Z scores from these two IVW methods are moderately positively correlated . In fact, the cross-biobank heterogeneity is often larger than the cross-ancestry heterogeneity for TWAS associations of A1BG. ZNF665, another gene with concordant associations across IVW strategies, showed a low heterogeneity in the per-biobank effect sizes (I2 = 0.26). However, genes with discordant associations across IVW strategies showed varied patterns. Two illustrative examples are MLNR and MYOZ3, both with large degrees of test statistic heterogeneity . Across the two IVW strategies, effect sizes are in opposite directions, possibly due to large standard error differences across the ancestry-specific per-biobank associations. A thorough investigation of the power and false discovery rates of these meta-analysis strategies through simulations is necessary. Methods that incorporate the per-biobank uncertainty must be explored to increase power and properly leverage the large sample sizes of GBMI.However, it is unclear whether ancestry-specific IVW meta-analysis to the per-biobank level is necessary. As shown in In addition to multi-ancestry populations, analyzing genetic data from individuals of admixed ancestry is also an open area of study. In this analysis, we have used the 1000Genomes AFR LD reference panel as an estimate of the LD for AFR ancestry samples from each biobank. However, most of GBMI’s AFR ancestry populations are of admixed ancestry . A single LD reference panel of AFR ancestry may not reflect the genetic diversity in these admixed populations of AFR and EUR ancestries.Z score to this null distribution assesses how much signal is added by the expression given the specific GWAS architecture of the locus. This permutation test is highly conservative and prioritizes only associations already significant in the standard TWAS GTA detection.TWAS GTAs identified using GWAS summary statistics are subject to several factors that may lead to false positives. We implement several follow-up tests to provide context to TWAS-identified GTAs. First, a TWAS GTA could attain significance due only to strong SNP-trait associations from the underlying GWAS. To quantify the significance of the GTA conditional on the SNP-trait effects at the locus, we perform a permutation test by permuting the SNP-gene weights from the expression model to generate a null distribution . CompariNext, gene expression models for genes in adjacent genomic windows may be built from overlapping SNPs or SNPs in strong LD. When TWASs detect GTAs in overlapping genomic regions, we apply probabilistic fine-mapping using FOCUSin vitro.The GBMI TWAS pipeline incorporates gene expression models using MOSTWAS, which prioritizes distal-eQTLs by testing their mediation effect through local molecular features . For genLastly, TWASs suffer from severely reduced power and inflated false positives in the presence of SNP horizontal pleiotropy.Biobanks aggregated in GBMI provide a rich catalog of phenotypes for analysis, with phenotype codes (phecodes) aggregated from ICD codes classified into clinically relevant categories.TAF7, a novel gene in our TWAS, and ILRAP18 and TMEM258, two genes previously implicated through GWASs.TAF7 (MOSTWAS model), IL18RAP (JTI model), and TMEM258 (JTI model). European-specific TWAS meta-analysis for asthma detected a negative association with TAF7 cis-GReX, a gene that did not intersect a GWAS-significant locus. In TWAS follow-up tests, TAF7 passed permutation testing and was estimated in the 90% credible set at the genomic locus via FOCUS with posterior inclusion probability 1. As the clinically relevant associations for TAF7 lung GReX are not characterized, we employed a GReX-PheWAS in UKBB European ancestry GWAS summary statistics across 731 traits and diseases with sample sizes greater than 100,000, grouped into nine categories . We also detected several associations with related respiratory diseases and traits. Similarly, for the two previously implicated genes, we find enrichments for respiratory and hematopoietic GTAs for ILRAP18 and across multiple categories for TMEM258, consistent with the categorized functions and associations of these genes , we will extend our TWAS analyses to interrogate underlying transcriptomic mechanisms underlying the complex traits studied with GBMI. We outline several methodological gaps to be addressed in the future: (1) training expression models that are portable across ancestry groups, (2) limiting false discovery in TWASs by properly modeling differences in LD across ancestry groups, (3) incorporating uncertainty within and heterogeneity across biobanks to boost TWAS meta-analytic power, and (4) contextualizing TWAS GTAs through follow-up testing, probabilistic fine-mapping across ancestry groups and expression contexts, and GReX-PheWASs.,,,,Along with the discussed issues with current TWAS methodology, tissue-specific expression may not provide sufficient granularity needed to discover trait-relevant biological mechanisms. Recent methods that study the mediation of the SNP-trait relationship by cell-type heterogeneity show that cell types and cell states are influenced by genetics and predict complex traits, and modeling these directly may lead to improved power in detecting trait associations.Despite the limitations of this suite of methods, TWASs continue to be a useful tool for interpreting GWAS associations and independently discovering genetic associations mediated by gene expression. More sophisticated integrative computational and experimental tools to complement improved TWASs and GWASs to understand the biology underlying health and disease need to be developed. Most importantly, reference eQTL data from individuals of non-European ancestry needs to be collected at parity with those of European ancestry individuals.We conclude with a few limitations of our evaluations of TWASs. We use eQTL data from GTEx to assess portability of expression prediction models, which have limited AFR ancestry sample sizes. These analyses should also be performed in larger cohorts of non-European populations, even though our results reflect previous evaluations. In addition, we evaluate only traditional TWAS methods. Though other methods are similar to these first TWAS methods, other frameworks may be better equipped to harmonize expression prediction and GTA mapping across ancestry groups. Lastly, we present only fixed effect meta-analyses. Meta-analysis with random effects models may prove to be more powerful.Kuan-Han H. Wu, Humaira Rasheed, Kristin Tsuo, Ying Wang, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N. Wolford, Valeria Lo Faro, Esteban A. Lopera-Maya, Kristi Läll, Marie-Julie Favé, Sinéad B. Chapman, Juha Karjalainen, Mitja Kurki, Maasha Mutaamba, Juulia Partanen, Ben M. Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A. Feng, Christopher R. Gignoux, Sarah E. Graham, Whitney E. Hornsby, Nathan Ingold, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y. Millwood, Priit Palta, Anita Pandit, Michael H. Preuss, Unnur Thorsteinsdottir, Jasmina Uzunovic, Matthew Zawistowski, Xue Zhong, Archie Campbell, Kristy Crooks, Geertruida H. de Bock, Nicholas J. Douville, Sarah Finer, Lars G. Fritsche, Christopher J. Griffiths, Yu Guo, Karen A. Hunt, Takahiro Konuma, Riccardo E. Marioni, Jansonius Nomdo, Snehal Patil, Nicholas Rafaels, Anne Richmond, Jonathan A. Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Kathleen C. Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Hilary K. Finucane, Lude Franke, Andrea Ganna, Tom R. Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Jukka T. Koskela, Clara Lajonchere, Matthew H. Law, Liming Li, Cecilia M. Lindgren, Ruth J.F. Loos, Stuart MacGregor, Koichi Matsuda, Catherine M. Olsen, David J. Porteous, Jordan A. Shavit, Harold Snieder, Richard C. Trembath, Judith M. Vonk, David Whiteman, Stephen J. Wicks, Cisca Wijmenga, John Wright, Xiang Zhou, Philip Awadalla, Michael Boehnke, Daniel H. Geschwind, Caroline Hayward, Kristian Hveem, Eimear E. Kenny, Yen-Feng Lin, Reedik Mägi, Hilary C. Martin, Sarah E. Medland, Yukinori Okada, Aarno V. Palotie, Serena Sanna, Jordan W. Smoller, Kari Stefansson, David A. van Heel, Robin G. Walters, Sebastian Zöllner, Biobank Japan, BioMe, BioVU, Canadian Partnership for Tomorrow’s Health/Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, QIMR Berghofer Biobank, Taiwan Biobank, The HUNT Study, UCLA ATLAS Community Health Initiative, UK Biobank, Alicia R. Martin, Cristen J. Willer, Mark J. Daly, and Benjamin M. Neale.abtbhatt@ucla.edu).Further information and requests for data availability and code should be directed to and will be fulfilled by the lead contact, Arjun Bhattacharya We first outline the steps of the TWAS pipeline employed for phenotype available for analysis in the GBMI. Then, we provide details for the analyses presented in cis-regulation across tissues,−6) as in previous TWAS analyses,,Tissue-specific expression models trained with reference data from the Genotype-Tissue Expression Project (GTEx) v8,To test for an association between tissue-specific GReX of a gene and a trait of interest, GWAS summary statistics are integrated with these expression models. For the EUR-specific TWAS, we use EUR-specific meta-analyzed GWAS summary statistics across all biobanks. JTI and MOSTWAS use two different approaches to test for a GTA. For MR-JTI, the posterior predictive distribution of GReX is estimated, and multiple-instrumental-variable causal inference is used to estimate the GTA, controlling for overall heterogeneity.Follow-up tests and analyses are then run to provide context to the TWAS GTAs. A permutation test is run by shuffling the SNP-gene weights 1,000 times and determining the TWAS Z score at each permutation, generating a null distribution. The original TWAS Z-score is compared to this null distribution to generate a permutation p-value; Benjamini-Hochberg FDR correction is used to account for multiple testing burden here. This test examines whether the SNP-gene relationship provides more information than just the SNP-trait association. Next, for MOSTWAS, the distal-SNPs added-last test is run to measure the association from distal-SNPs in the expression models, conditional on the association from local-SNPs.2 to account for sample size, using an appropriate linear model between predicted and observed expression. For imputation samples that are used in training , we use 5-fold CV when measuring predictive performance. We also conducted a down-sampling analysis, where we randomly selected a subset of the EUR samples to match the AFR sample size; in this down-sample, we performed the same comparison of predictive performance. Lastly, when imputing into AFR and EUR samples using the ancestry-unaware models, we only cross-validate over the AFR or EUR samples, respectively. For example, the training set in a fold when imputed into AFR samples includes all EUR samples and 80% of the AFR samples.To show the utility of ancestry-specific models, we train EUR- and AFR-specific models using elastic net regression for 5 tissues with more than 70 samples from AFR ancestry patients. To balance sample sizes in the imputation sample, we down-sampled the EUR ancestry imputation sample to match the AFR imputation sample. We consider only genes with positive expression heritability in both EUR and AFR training samples. We also build ancestry-unaware models, where genotypes for EUR and AFR samples are pooled together in the training sample. We calculate predictive performance in aligned and misaligned imputation samples based on ancestry; the aligned imputation sample is one with ancestry that predominantly matches the ancestry of the training sample. Predictive performance is measured with adjusted RWe compared 5 different meta-analytic strategies empirically: meta-analyzing across ancestry-specific, per-biobank GWAS summary statistics using (1) inverse-variance weighting (IVW) and (2) sample-size weighting (SSW), meta-analyzing across ancestry-specific meta-analyzed GWAS summary statistics using (3) IVW and (4) SSW, and (5) TWAS using ancestry-unaware models into meta-analyzed GWAS summary statistics across EUR and AFR ancestry groups. First, we consider three different sets of GWAS summary statistics: biobank- and ancestry-specific summary statistics, ancestry-specific summary statistics meta-analyzed across all biobanks, and summary statistics meta-analyzed across biobanks and ancestry groups. In two former settings, for biobank Here, we define the effective sample size as For the ancestry-unaware TWAS, we use ancestry-unaware elastic net regression models and integrate with GWAS summary statistics meta-analyzed across all ancestry groups and biobanks.TAF7 (MOSTWAS model), IL18RAP (JTI model), and TMEM258 (JTI model). A phenome-wide significant association was defined via Bonferroni correction (P < Transcriptome-wide significant genes are further prioritized by performing GReX-PheWAS to categorize associations across a broad spectrum of phenotypes. Using UKBB summary statistics from European ancestry patients,


Ideotype breeding is an essential approach for selection of desired combination of plant traits for testing in crop growth model for potential yield gain in specific environments and management practices. Here we parameterized plant traits for untested lentil cultivars for the APSIM-lentil model in phenology, biomass, and seed yield. We then tested these against independent data and applied the model in an extrapolated analysis (i) to assess the impact of drought on productivity across different rainfall environments; (ii) to identify impactful plant traits and (iii) to design new lentil ideotypes with a combination of desirable traits that mitigate the impact of drought, in the context of various agronomic practices across a wide range of production environments. Desirable phenological and physiological traits related to yield were identified with RUE having the greatest effect on yield followed by HI rate. Leaf size significantly affected seed yield (p< 0.05) more than phenological phases. The physiological traits were integrated into four ideotype designs applied to two baseline cultivars  providing eight ideotypes. We identified a combination of genetic traits that promises a yield advantage of around 10% against our current cultivars PBA Hallmark XT and PBA Jumbo2. Under drought conditions, our ideotypes achieved 5 to 25% yield advantages without stubble and 20 to 40% yield advantages with stubble residues. This shows the importance of genetic screening under realistic production conditions . Such screening is aided by the employment of biophysical models that incorporate both genetic and agronomic variables that focus on successful traits in combination, to reduce the impact of drought in the development of new cultivars for various environments. Stubble retention was found to be a major agronomic contributor to high yield in water-limiting environments and this contribution declined with increasing growing season rainfall. In mid- and high-rainfall environments, the key drivers of yield were time of sowing, physiological traits and soil type. Overall, the agronomic practices, namely, early sowing, residue retention and narrow row spacing deceased the impact of drought when combined with improved physiological traits of the ideotypes based on long term climate data. Lentil is a cash crop and provides rotational benefits in cereal-based farming system . The croPrevious studies have shown the advantage of agronomic practices, such as stubble retention in improving soil water storage by reducing losses through evaporation in the dry environments. For example, However, in environments, where multiple constraints are limiting crop production , a key uSeveral studies have used crop growth models to understand the interactions between plant trait by environment by agronomic practice for desirable trait optimization under water limited environments . This apUsing global sensitivity analysis and crop simulation modelling techniques, In this study we developed new parameters for untested lentil cultivars for the APSIM-lentil model in phenology, biomass, and seed yield. We then tested these against independent data and applied the model in an extrapolated analysis (i) to assess the impact of drought on productivity across different rainfall environments; (ii) to identify impactful plant traits and (iii) designed eight new lentil ideotypes with a combination of desirable traits that mitigate the impact of drought, in the context of various agronomic practices across a wide range of production environments.Three locations selected for field experiments were Hopetoun (35°42’10”S 142°27’26.0”E) and Beulah (35°59’52.5”S 142°33’12.7”E), representing low rainfall  environments, and Horsham (36°44’10.8”S 142°06’25.4”E) which represented a medium rainfall environment (300 – 400 mm) in Victoria, Australia. In 2020, time of sowing trials were carried out at Horsham and Hopetoun for model parameterization and testing (Experiment 1). Field experiments conducted between 2016 and 2020 at Horsham and Beulah were used for model validation (Experiment 2).th of April, TOS2 on the 18th May and TOS3 on the 28th of May, representing early, optimum and late time of sowings) at each location in 2020. The experiments were arranged in a split-plot design with three replications with time of sowing as main-plots and genotype as sub-plots.Five commercially released varieties , and one breeding line (12H681L-5-15HSHI3012) of lentil were evaluated at three times of sowing (TOS) where TOS1 was on the 28Independent field trials were carried out at Horsham and Beulah between 2016 and 2020. The trials were conducted as Randomized Completed Block Designs, with three replicates. The datasets of five varieties (matching experiment 1) were used for model validation for days to 50% flowering and seed yield.2 in both Experiment 1 and Experiment 2. The experimental unit area was 5m x 1.25m with five rows per plot. Crop management practices, disease, weed and insect controls were carried out as per farmer best practices.The trials were planted at 120 plants per mThe datasets from these two experiments were used for parameterizing (Experiment 1) and testing (Experiment 2) the ability of the APSIM-lentil model to predict seed yield and yield related parameters of new varieties of lentil.https://www.apsim.info/documentation/model-documentation/crop-module-documentation/plant/).There are several modules linked to the APSIM model to simulate crop growth and development. Cultivar, climate and soil modules contain variety and site-specific parameters connected to the central system of the model. The key specific model parameters, including (i) soil information , (ii) crop and genotype specific coefficients and (iii) daily climate information  are required to simulate crop growth and development. The key physiological principles applied to all crops for simulation are the same across different crop species except some specific parameters, such as the genotypic coefficient, can be measured and included into the cultivar module through parameterization  were taken by moving in diagonal line across the experimental paddock and taking three soil cores at equal points. Detailed information and methods of data analysis for the soil parameters followed the analytical laboratory procedure .Soil water content at sowing were determined using the gravimetric method at the following soil depths: 00-15, 15-30, 30-50, 50-70, 70-90, 90-110 and 110-130. The samples were oven dried at 105°C for 48 hours and soil water content was determined following the soil matters book procedure . The dathttps://greenbrain.ag/documents/mea-weather-station-brochure.pdf) located within 0.5-km radius of the trial.Meteorological data  was measured daily at each experimental site using an automatic weather station .Key phenological stages were recorded and the duration between each stage was determined by cumulative thermal time (growth degree days) except the period between sowing to germination, which is governed by soil moisture and soil temperature . Sowing Cumulative thermal time in growth degree days (GDD) between each growth stage was calculated by summing daily values of thermal time, taking into account the three cardinal temperatures: 0.0 (base temperature or the lower limit), 30.0 (optimum for growth), and 40.0°C (the upper limit) using a sine curve approach , where tth node appeared, and floral initiation at the 14th node. Flowering and physiological maturity stages of the crop were recorded when 50% of the plants in the plot had at least one flower open and when 90% of the plants and pods had turned yellow. Aboveground biomass was also harvested six times between the start of grain filling and physiological maturity to determine harvest index rate.Phenologically, the end of juvenile stage was measured when the 8We parameterized cultivar specific parameters related to biomass accumulation and seed yield from field trials conducted under optimum field conditions. These parameters were radiation use efficiency (RUE), harvest index rate and leaf size.During the seed formation phase, aboveground biomass was harvested between start of grain filling and physiological maturity to determine harvest index . The daily harvest index rate was estimated from the slope of the line drawn between days after sowing and harvest index.Leaf area was measured on 90 representative leaf samples of each variety using a leaf area meter  along with the number of nodes at different growth stages.Incident and upcoming photosynthetically active radiation (400-700nm) were measured using a ceptometer  once every fortnight on the TOS2 trial. The fraction of intercepted photosynthetically active radiation (fiPAR) was calculated as in eq.1. The daily cumulative iPAR was estimated from daily solar radiation multiplied by daily fiPAR. The RUE was estimated from the slope of the line drawn between aboveground biomass and cumulative intercepted photosynthetic active radiation for each genotype to determine daily biomass accumulation in the model .https://www.apsim.info/documentation/model-documentation/.The model was used to simulate various parameters of selected lentil varieties to time of sowing experiments under different environmental conditions at various TOS’s in Hopetoun and Horsham in the 2020 and 2021 growing seasons. Other datasets derived from National Lentil Breeding Program trials conducted at Beulah and Horsham between 2016 and 2020 were also used for validation. The same agronomic practices from field experiments conducted at Hopetoun and Horsham in 2020 and 2021 crop growing seasons were used in relation to sowing characteristics, plant population, type and rate of fertilizer. Further information of the APSIM lentil module is found at: The APSIM model capability of reproducing the experimental data in lentil trials was tested using the root mean square error (RMSE), normalized root mean square error (NRMSE), Pearson coefficient (R), Nash Sutchiffe model efficiency coefficient (ME), index of agreement (d) and Lin’s concordance correlation coefficient (CCC) by comparing the measured and modelled values. The model accuracy was evaluated as indicated in A factorial combination of plant traits/ideotype and agronomic practices  was designed and run using the APSIM-lentil model across multiple environments. Five times of sowing , two stubble management , two row spacings (narrow row spacing = 19cm & wide row spacing = 38cm) and two varieties of lentil , including ideotypes and physiological traits were included in the scenario analysis. Overall, 7,249,500 simulated seed yields of lentil were generated using 50-years of historical climate data across 15-sites in Wimmera and Mallee environments in Victoria. Based on trait-yield relationship, potentially important plant traits were identified, and genetic diversity of the traits were created by a ten percent increase (10%-40%) around the baseline variety. The traits were combined with best-bet agronomic practices and evaluated across multiple sites and years for yield and plant available water improvement. In this study, 750 environments were clustered into three rainfall conditions  using percentiles. The average yield of high rainfall environments were used to compare to average water-limited yield potential of each region and quantify the impact of drought. The formula of these sensitivity indices are presented in eq 2 and eq 3 as in ; 2022 ani; E[YieldX-i] = yield across all sources of variance without Xi; Contribution of each factor to yield variability is explained by the main effect (ME), while the interaction effect is explained by total effect (TE), a factor with a high proportion of the total variance is highly interactive with other factors.Where, E[YieldXi] = yield across all source of variance XA new APSIM-lentil model using an existing APSIM-chickpea background  has beenSoil parameters used to simulate Hopetoun and Horsham experiments were collected and converted into APSIM format . Overall, the node appearance on mainstem of lentil was linearly and positively associated with growth degree days , where PBA Jumbo2 and 12H681L-5-15HSHI3012 had relatively slower node appearance rates  in six lentil genotypes evaluated in experiment 1. Node appearance rates of high yielding genotypes (PBA Jumbo2 and PBA Kelpie XT) were slightly lower compared to the rest of the genotypes at early reproductive stage  to 19 days (TOS3) with the corresponding accumulated daily degree days of 171 (TOS1) – 179 °Cd (TOS3) at 50mm sowing depth (Experiment 1). However, the model underestimated days to emergence using the default values. For example, 8 days for TOS1 and 9 days for TOS3 were simulated using the default values of pre-emergence shoot rate (1 °Cd/mm) and shoot lag (15 °Cd) at a sowing depth of 50 mm.The accuracy of model prediction for days to emergence was improved  and at floral initiation (14th node). PBA Jumbo2, PBA Kelpie XT and PBA Ace had higher individual leaf area than the rest of the genotypes  was also included in lentil parameterization using the TOS2 trial at Horsham 2020. Pooled leaf size (n=90) for each variety was calculated at the vegetative stage  at Horsham and Beulah and physiological maturity (DM) at Horsham for all 6 varieties. The RMSEs were less than 5 days for DF at each season and the model predicted DF with high accuracy across all seasons at both Horsham  and Beulah   and late  time of sowing as shown in The APSIM-lentil model was evaluated for six genotypes using datasets from ten field trials. Measured and simulated grain yields were compared for different genotypes, time of sowings, seasons, locations, and soil types. Measured seed yields across the seasons ranged from 1.4 - 4.19 t/ha at Horsham, 1.66 - 2.29 t/ha at Hopetoun and 0.78 - 3.93 t/ha at Beulah compared to simulated yields from 1.61 - 4.33 t/ha at Horsham, 1.31 - 2.88 t/ha at Hopetoun and 1.77 - 4.20 t/ha at Beulah.The model predicted seed yield with high accuracy Figure 2Pearson correlation was applied to assess the associations between plant traits and yield. Desirable phenological and physiological traits related to yield were identified Table 2 In this study, the validated model was used to assess the impact of drought on lentil yield using 750 environments (15-locations and 50-years) between 1971 and 2020. Overall, lower yields were predicted in low rainfall  than in medium rainfall (Wimmera) environments. Yield losses due to drought were shown to substantially increase over decades, particularly in the last two decades in all sites Figure 3Further analysis was performed to quantify the impact of physiological traits and agronomic practices on plant available water and seed yield of lentils across different rainfall environments. Main effect and total effect were further analyzed using ideotypes created from physiological traits  by a ten per cent increase around the baseline varieties, PBA Hallmark XT and PBA Jumbo2. The contribution of ideotype to plant available water and seed yield variance were greater than 20% and 15%, respectively Figure 5Predicted yield losses were decreased by early sowing and growing improved ideotypes in both Mallee and Wimmera environments . Such screening is aided by the employment of biophysical models that incorporate both genetic and agronomic variables that focus on successful traits in combination, to reduce the impact of drought in the development of new cultivars for various environments. Traditionally, plant breeders select high yielding breeding lines for germplasm enhancement and variety development through long cycle recurrent selection approaches. This method is not only resource inefficient but also very expensive in development of varieties. Our coupled modelling approach is a powerful decision-making tool to aid breeders in defining traits of interest and varietal choice for targeted environments and production constraints as advocated by others for different crops .The current study presents a summary of cultivar specific coefficients derived from field experiments for commercially released varieties of lentil crop. To the best of our knowledge, this is the first study to measure and present more detailed variety specific harvest index rates, RUE, leaf size and phenology to be used in the APSIM-lentil cultivar module. We found clear differences among varieties evaluated for the plant traits and this is a baseline information to be used for further application in the APSIM-lentil model. Overall, the APSIM-lentil model simulated phenology, biomass, and seed yield with a good accuracy across our testing sites. This finding is consistent with previous results of modelling phenology and yield in lentil .The proposed lentil ideotypes in two different physiological backgrounds reduced yield loss more in medium than in low rainfall environments, indicating the importance of physiological traits in maximizing yield in environments of higher rainfall. Stubble retention decreased yield loss more in low than medium rainfall environments relative to the baseline varieties. The synergies between both ideotype and stubble retention reduced yield loss by twofold in medium over low rainfall environments. Previous studies have also shown the adaptation of an ideotype and agronomic practice to drought in different crops .The combination of ideotypes and early time of sowing also reduced yield losses to drought in both low and medium rainfall environments. In other studies, delays of sowing by a week in this region had a yield penalty of 5-12% in canola crop  while adOur result found early sowing  in lentils as the optimum window for most sites and growing seasons of low and medium rainfall environments of South-eastern Australia. Similar to our result, 25 April to 9 May of optimum sowing period of wheat was recommended for Wimmera and Mallee regions . Based oIn general, lentil yield in low rainfall environments were largely improved due to improvements in agronomic practices, unlike findings in high rainfall environments. Improvement in physiological traits of leaf size, RUE and HI rate are regarded as the more promising traits related to yield improvement of lentils in high rainfall environments. Agronomic practices are the most adaptive strategy to reduce risks of drought in water limited environments. Moreover, the suitable match between agronomic practices and physiological traits are a useful adaptation approach to mitigate the impact of drought in lentils production and the current framework can be applied in other pulse crops for yield improvement.Moreover, we have shown that increasing trait values in an ideotype can improve yield potential. Evidently, physiological trait based ideotype breeding can support selection for a conventional breeding method by first designing efficient combinations of plant traits that improve yield potential of lentils based on our current study; however, there is risk that some traits may be physiological difficult to achieve . Clearly, some optimization of trait combination might be needed where all possible combinations including down-regulated traits are considered. This can potentially be supported by high throughput phenotyping and machine learning algorithms to make the method more efficient to achieve productivity advancement.Ideotype breeding that combines phenological traits and agronomic practices is another potential area to optimize yield of lentils through minimizing the negative effects of production constraints. Crop growth model aided ideotype breeding is a useful approach to explore the optimal growing window of lentil by matching crop phenology with risk free periods of frost, heat, water and combinations of these stresses through varying the phenological traits of crop varieties in a given environment . For exaUnderstanding phenological traits is the first and the most basic step for ideotype design and development, where drought and other factors affect lentil productivity. However, characterization of lentil for phenological traits still requires a significant effort. In this study, we characterized the phenological traits of six varieties of lentils and these traits can be used for ideotype design and testing to maximize yield of lentils under abiotic production constraints. However, crop growth model based ideotype breeding lacks the capability for predicting the effects of biotic factors such as disease, insect pests as well as quality traits for ideotype breeding .Recent progress in process-based crop simulation modelling opens the way for better understanding of the drivers of crop productivity and the suitable match of physiological traits and agronomic practices to optimize yield . In the The advantage of stubble retention in dry environment where soil water storage can be increased (e.g. in clay soils) is related to improved soil water infiltration, and reduced water loss to evaporation .Conservation of water with stubble retention is not as critical in higher rainfall environments and a decline in the contribution of stubble retention on plant available water and yield is likely. Unlike low rainfall environments, time of sowing and physiological traits  were found to be important factors in non-droughted environments, while this effect tended to decline with decreased rainfall. In addition, medium to late maturing varieties could also be beneficial to increase crop productivity in high yielding environments .In drier environments, large leaf size and fast leaf development were good traits for early vigor and better weed control during early crop growth. In addition, leaves are the primary part of a plant for photosynthesis, where plants maximize the use of the intercepted photosynthetic active radiation (PAR) and convert water and carbon dioxide into biomass and yield in high resource input environments. A large leaf size has been related to high photosynthetic capacity of individual leaf  in high The current study computed node appearance, which varied with varieties from 57 to 61°Cd. However, in the APSIM version 7.10, the default value for the node appearance rate of lentil has been set at 61°Cd regardless of varietal differences. Based on our results, the node appearance on the main stem tended to have a faster rate for stay green late maturing varieties, such as Nipper, PBA ACE, and PBA Hallmark XT. The phenological development in the APSIM-lentil model is defined by eleven growth stages. In this study we have characterized the key phenological phases of lentil as required in the APSIM framework. The lag phase, a slow growth phase of shoot elongation between sowing and germination is driven by cultivar specific fixed thermal time . A cumulThe current study also used data from field experiments and described phenological stages of lentil as defined in APSIM. The duration between emergence and end of juvenile phase is driven by thermal time. We calculated growth phases from emergence to end of juvenal phase and from end of juvenile phase to floral initiation in terms of cumulated thermal time. Measured duration between stages varied with cultivar, where emergence to end of juvenile ranged between 559 °Cd and 681 °Cd, end of juvenile to floral initiation ranged between 116 °Cd and 277 °Cd and start pod fill to end of pod fill ranged between 545 °Cd and 679 °Cd. Unlike our results, a recent study by To the best of our knowledge, this is the first study to measure and present variety specific harvest index rates to be used in the APSIM-lentil model module. We found clear differences among varieties evaluated for harvest index rate. Studies have shown improvement in seed yield due to increase in HI  and thisOverall, modern varieties had higher genotypic coefficients of physiological traits. From this result, it appears that the modern-day varieties yield gain could be driven by improvement in physiological traits.APSIM-lentil model was parameterized for the first time in detail to improve model prediction. The stepwise process of parametrization employed provided robust cultivar specific coefficients that are suitable for wider ideotype investigations with the APSIM-lentil model. The stepwise process required initial measured soil water at our experimental sites utilizing nearby APSoil parameters. Overall, the APSIM-lentil model performed well in predicting phenology, seed yield and biomass and the model should be applicable to other soil types of South-eastern Australia and possibly other similar environments. A better understanding of ideotype and agronomic practices synergies using crop modelling helped to identify high yield potential of lentil crops in specific environments. Agronomic options  together with improved physiological traits can be used to reduce the impact of drought stress in lentils. In a low rainfall environment, stubble retention and time of sowing are more important than physiological traits/ideotypes. The ideotype concept in lentil breeding should significantly support breeders to define traits of interest and varietal choice for targeted environments and production constraints. Unlike in a low rainfall environment, selection for physiological traits is important for yield improvement in lentil crops grown in medium and high rainfall environments. The present study identified ideotype designing coupling with agronomic practices adjustment as a very important adaptation strategy to reduce the impact of drought.The original contributions presented in the study are included in the article/Conceptualization and methodology, GR, GO’L, and AT. Funding acquisition and supervision, GR. Data collection, analysis, modelling and writing of the first draft, AT. Revisions, GO’L, GR, and RA. Assisted in crop modelling, GO’L and TT. Assisted in data collection, SR and VS-P. Edition and comments, all authors. All authors contributed to the article and approved the submitted version.The study was supported by Agriculture Victoria and the Grain Research and Development Corporation.We thank the Agriculture Victoria (AV) and the Grain Research and Development Corporation (GRDC), Australia for supporting this project (Project VGIP1B- IDGRDC 8049295), Physiological and agronomic approaches to achieve transformational productivity improvements in pulses in southern Australia and contribution from associated project VGIP2B Intercropping to exploit rainfall for profit. We also thank the Molecular Plant Breeding team of Horsham Grain Innovation Park for their technical support, particularly to the VGIP1B team, Tamika Mentha, Jignesh Vakani and Sunita Bastakoti Pandey.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.