The two breeds could be clearly distinguished using multidimensional scaling plots and admixture analysis. Considering the top 0.0005% FST values, a total of 64 markers were detected in the single‐marker analysis. The top FST value was detected for the melanocortin 1 receptor (MC1R) gene mutation, which determines the red coat colour of the Reggiana breed. Another coat colour gene, agouti signalling protein (ASIP), emerged amongst this list of top SNPs. These results were also confirmed with the window‐based analyses, which included 0.5‐Mb or 1‐Mb genome regions. As variability affecting ASIP has been associated with white coat colour in sheep and goats, these results highlighted this gene as a strong candidate affecting coat colour in Modenese breed. This study demonstrates how population genomic approaches designed to take advantage from the diversity between local genetic resources could provide interesting hints to explain exterior traits not yet completely investigated in cattle.Autochthonous cattle breeds are genetic resources that, in many cases, have been fixed for inheritable exterior phenotypes useful to understand the genetic mechanisms affecting these breed‐specific traits. Reggiana and Modenese are two closely related autochthonous cattle breeds mainly raised in the production area of the well‐known Protected Designation of Origin Parmigiano‐Reggiano cheese, in the North of Italy. These breeds can be mainly distinguished for their standard coat colour: solid red in Reggiana and solid white with pale shades of grey in Modenese. In this study we genotyped with the GeneSeek GGP Bovine 150k single nucleotide polymorphism (SNP) chip almost half of the extant cattle populations of Reggiana ( Many local breeds have been developed by the combined action of several factors and events mainly driven by the recent interplay between economic, social and environmental conditions that contributed to define their genetic history  Parmigiano‐Reggiano cheese, in the North of Italy. Reggiana and Modenese are historically considered the ancestral cattle populations from that this cheese has been originated. Their names derive from the two geographically close provinces of Reggio Emilia and Modena, located in the Emilia‐Romagna region where they have been mainly originated and where most of the farms raising Reggiana and Modenese cattle are now localized. The Herd Book of these two breeds were officially recognized in 1962 (Reggiana) and in 1957 (Modenese). In recent history, between the 1980’ and in first years of the 2000, both breeds have experienced a progressing decline of the population size that reached a minimum of approximately 500 Reggiana heads and 260 Modenese heads. Since then, the population size has been increasing. The recovery of these two breeds can be mainly attributed to the development of two mono‐breed branded Parmigiano‐Reggiano cheeses that can be produced from milk of only Reggiana cows or from milk of only Modenese cows. The market prize of these niche products is higher than the undifferentiated Parmigiano‐Reggiano cheese obtained by cosmopolitan breeds, hereby compensating the reduced milk yield that characterize these two breeds  allele caused by a frameshift mutation  has been indicated to determine the red coat colour in Reggiana cattle . A genome‐wide association study for exterior traits has been recently carried out in Reggiana  were genotyped with the GeneSeek GGP Bovine 150k SNP chip following the manufacturer's protocol. PLINK software v. 1.9 , 326 Modenese samples and a total of 128,574 markers.After filtering, the data set accounted for 1109 Reggiana samples  were calculated with PLINK v. 1.9 , fixation index (FST) and inbreeding coefficient of an individual (I) relative to the total (T) population (FIT) were calculated with VCFtools software  was measured using 2r threshold of 0.1). A total of 14,131 SNPs was retained (average of 487 ± 166 SNPs for each chromosome). Multidimensional scaling (MDS) analysis was performed using a matrix of genome‐wide identity‐by‐state (IBS) pairwise distances as implemented in PLINK 1.9  single‐marker pairwise FST analysis and (ii) averaged genome window FST comparative analysis. The SNP with the top 0.0005% FST  defined the threshold to detect signatures of selection. In the window‐based approach, 4,987 windows of 1 Mb with a step of 500 kb, were tested by computing an average FST based of SNPs overlapping the window. A total of 9,953 windows of 500 kb with a step of 250 kb were also tested. Analyses, based on method of Weir and Cockerham (ST (mFST) 20 windows were considered in these analyses .Signatures of selection were determined using pairwise Fockerham  were perockerham . All winBos taurus genome assembly version ARS‐UCD1.2 and considering a region of ± 200 kb around the detected SNPs or considering the overlapping or partially overlapping windows. Genes were retrieved using Ensembl Biomart tool (http://www.ensembl.org/biomart/martview/) and then evaluated considering their functional roles according to an extensive literature search.Annotation of the genome regions including the SNPs and windows that trespassed the defined threshold was retrieved from the ST analysis was performed with Enrichr , KEGG v.2021 (http://www.kegg.jp/) and GWAS catalogue v.2019 (https://www.ebi.ac.uk/gwas/) human libraries. Terms with an adjusted p‐value <0.05 and at least two input genes were retained as statistically over‐represented.Functional gene enrichment analysis of genes closed to the SNPs detected in the single‐marker FMC1R and ASIP gene regions was performed using the software Haploview v. 4.2  with the lowest MAF values (ranging from 0.01 to 0.05) that was detected in the Modenese breed than in Reggiana . Within‐breed HO and HE heterozygosity were lower in Modenese than in Reggiana  trespassing the 99.95th percentile threshold is reported in Table ST values in these windows ranged from 0.344 to 0.222 in the top (99.8th percentile) 0.5‐Mb genome windows and from 0.255 to 0.163 in the top (99.6th percentile) 1‐Mb genome windows. The drastic drop of FST values from the single‐marker to the window‐based analyses might indicate that the two breeds could be distinguished by a few highly separated loci that experienced a rapid decay of LD apart from informative short genome regions, diluting the FST values in the window‐based approaches.Table ST = 0.977) was the frameshift mutation in the MC1R gene that causes the e allele at the Extension locus and that determines the classical red coat colour of the Reggiana cattle . This polymorphic site, located on BTA18, was in the first top genome window of the 0.5 Mb analysis and in the third and fourth top sliding windows in the 1 Mb analysis. The haploblock structure of this region in Reggiana cattle indicated that a relatively low level of LD is present in the MC1R gene region in both Reggiana and Modenese breeds, with some LD blocks only upstream or downstream this gene  gene  and voltage dependent anion channel 1 (VDAC1) genes.The second top polymorphism in the single‐marker analysis was localized on BTA7 about 9.6 kb from the is Table , were with percentile markers, that were also contained in top genome windows considering both the 0.5‐Mb and 1‐Mb size, were located on BTA13 within or very close to the eukaryotic translation initiation factor 2 subunit beta (EIF2S2) and agouti signalling protein (ASIP) genes  and eukaryotic translation initiation factor 2 subunit beta (EIF2S2) genes, which are upstream the ASIP gene  gene, which is downstream the ASIP gene  gene, SNPs on BTA6 were within the glutamate ionotropic receptor delta type subunit 2 (GRID2) gene and the marker on BTA24 was in a desert region where the closest genes are cadherin 2 (CDH2) and cadherin related 23 (CDH23)  Table .ASIP, ERBB4, EIF2S2 and MC1R genes. Melanogenesis was the most over‐represented KEGG pathway  whereas the regulation of tyrosine phosphorylation of STAT protein  was the most over‐represented GO Biological Process.Gene enrichment analysis returned significant results only for the Human GWAS catalogue. Amongst the 12 over‐represented phenotypes Table , nine we4Reggiana and Modenese are considered two iconic breeds that are part of the history of the livestock production sector of the North of Italy from which the well‐known PDO Parmigiano‐Reggiano cheese was originated. Genomic population parameters calculated for the two breeds are in agreement to those that usually describe the small population size of many autochthonous breeds.ST analyses, which are the commonly used methodologies for these types of investigations , as it could be expected, also resulted in a relatively high genetic closeness when Reggiana and Modenese were analysed together with many other Italian cattle breeds ; therefore, one of the main drivers that contributed to separate them and that represents the main characterizing phenotype is their different coat colour. Solid red (Reggiana) and solid white with grey shades (Modenese) are the colours that define the standards of these two breeds. This phenotype is the most relevant descriptor used to admit animals in one or the other breed herd book. There is a story telling tradition that suggests that the selection for different coat colours in Reggiana and Modenese would derive from the ancient rivalry between the two close towns (i.e. Reggio Emilia and Modena) from which the two breeds took their names.e allele of the MC1R gene, which causes the classical red (fromentino) coat colour of the Reggiana cattle  reached by the causative mutation at the MC1R allele dropped in the window‐based analyses, as it was averaged across all SNPs included in 0.5 Mb or 1 Mb. The relatively low LD that is present in the MC1R gene region of BTA18 in the Reggiana indicated an ancient origin of the fixed e allele in this breed and that more haplotypes or haplotype blocks containing this causative mutation were present in Reggiana cattle. The strong selection pressure that fixed  this Extension allele, therefore, did not result in an extended fixation of several other close SNPs on BTA18.The most relevant signature of selection that differentiated Reggiana and Modenese breeds was determined at the MC1R gene, as also reported in a previous study  in the SNP chip and/or to the masking effect of the mutated MC1R allele in Reggiana that would epistatically cover the mutated allele(s) at the ASIP gene. Mutated ASIP alleles could be also present in the Reggiana breed but at lower frequency than in Modenese breed, reducing in this way allele frequency differences between the two breeds and, in turn the FST values in this BTA13 region. The LD analysis in Reggiana and Modenese indicated high LD in different regions of the BTA13 that includes ASIP, suggesting the presence of different haplotype structures in the two breeds, with different potential regulatory effects over ASIP that we could hypothesize .Instead, the genetic determinism of the white coat colour with some pale grey shades of the Modenese cattle, has not been unravelled yet. This type of coat colour, also described in several other taurine breeds, have not been fully clarified in any other populations as well. Modenese breed is almost fixed for the wild type allele at the  Searle, . TherefoASIP gene determined by copy number variations (CNVs), probably with regulatory effects on gene expression, has been already associated with the white coat colour in different sheep and goat breeds . Recently, Trigo et al. (Bos indicus), which are selected for white coat colour, a structural variant affecting the ASIP gene expression is associated with darker coat pigmentation on specific parts of the body. Few studies have investigated variability in the Bos taurus ASIP gene. Royo et al  in determining the white coat colour of the Modenese cattle, no signatures of selection were determined in genomic regions harbouring other genes affecting coat colour in cattle. This would exclude the involvement of other well‐known genes determining white patterns, like the v‐kit Hardy‐Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) gene on BTA6 and the microphthalmia‐associated transcription factor (MITF) gene on BTA22  gene on BTA5, which was recently shown to dilute the coat colour in Reggiana cattle  allow the identification of hints that could explain their main phenotypic differences. Signatures of selection were evidenced in two genome regions encompassing major coat colour‐affecting genes. One region on BTA18, including the This study demonstrates how population genomic approaches designed to take advantage from the diversity between local genetic resources could provide interesting information to explain exterior traits not yet completely investigated in cattle.The authors declare they do not have any competing interests. Data reported in this work can be shared after signature of an agreement on their use with University of Bologna.L.F. designed the study, interpreted the results and obtained funding. L.F., F.B. and G.M. wrote the paper. A.R. and G.M. performed the wet lab work. G.M., F.B., G.S., S.B. and M. Ba. conducted bioinformatic analyses. M. Bo. and M.P. provided samples and data. S.B., G.S., S.D. and M. Ba. contributed to data interpretation. All authors read and approved the submitted version.Supplementary MaterialClick here for additional data file.


Bulk segregant analysis (BSA) can help identify quantitative trait loci (QTLs), but this may result in substantial bycatch of functionally irrelevant genes.CCA1, FT, and TSF were identified as having functionally significant variation compared to Arabidopsis. We validated and confirmed our ontology-mediated results through genome sequencing and homology-based SNP analysis. However, our ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery.Here we develop a Gene Ontology-mediated approach to zoom in on specific genes located inside QTLs identified by BSA as implicated in a continuous trait. We apply this to a novel experimental system: flowering time in the giant woody Jersey kale, which we phenotyped in four bulks of flowering onset. Our inferred QTLs yielded tens of thousands of candidate genes. We reduced this by two orders of magnitude by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. A pathway enrichment test then led to the circadian rhythm pathway. The genes that enriched this pathway are attested from previous research as regulating flowering time. Within that pathway, the genes Our method is potentially applicable to the study of other complex traits and we therefore make our workflows available as open-source code and a reusable Docker container.The online version contains supplementary material available at 10.1186/s13007-022-00921-y. Identifying the genes that underlie quantitative trait variation is one of the main challenges in genetics and, to the extent that this is attainable in silico, in bioinformatics. One appealingly straightforward approach to discovering candidate loci involved in quantitative trait differences is to sort individuals of a segregating, crossed population into pools defined by extremes in trait values and then interrogating the genetic contrasts between these pools, i.e. bulk segregant analysis BSA , 2). Hig. Hig2]).Several statistics have been developed to aid in the discovery of candidates of these needles. For each SNP in a sequenced pool, metrics exist that express its relative coverage compared to other pools (the Δ(SNP-index) Sensu Takagi et al. ) or whetBrassica oleracea cross. The remarkable variation in B. oleracea in flowering time is a critical agronomic trait. For example, whereas broccoli is a short-lived annual that flowers in the year it was planted, cabbage is biannual, needing a cold period to induce flowering  [Brassica has been advanced by the release of genomes from various species (e.g. [B. oleracea: the rapid cycling line TO1000DH3 [B. oleracea var. capitata [Brassica species is reasonably well characterized [FLOWERING LOCUS T (FT) locus, its transcriptional repressor FLOWERING LOCUS C (FLC), and its transcriptional activator CONSTANS (CO) all play a central role both in B. oleracea, B. rapa and in Arabidopsis. However, the way FT expression is modulated differs between Brassica and Arabidopsis. The overall flowering time pathway is much more complex in all cases, involving over two dozen other genes in multiple, divergent copies scattered across the genome [We apply and validate this approach using pools determined by contrasting flowering time in a ization) . Researces e.g. –10), inc, incBrasO1000DH3  and B. ocapitata . The gencterized . The FLOe genome . As the B. oleracea cultivars. This system is a useful test of the approach, as the regulation of flowering time in other cultivars is fairly well characterized [B. oleracea convar. acephala var. viridis), one of the two parents of the BSA crosses . For thd before  and compB. oleracea kale-like alboglabra line TO1000DH3 [B. oleracea convar. acephala var. viridis) native to Jersey , thWe genotyped the F1 population with an allele-specific assay (KASP) on our in-house high throughput SNP genotyping platform and phenotyped the plants on time till first flowering based on two individuals per genotype, distinguishing early (EF), intermediate (IF), late (LF) and non-flowering  cohorts Fig.  and incr® 96 Plant kit (Macherey–Nagel GmbH & Co.). We used a volume of 150 μl for elution. We measured DNA concentrations on a Dropsense (TRINEAN NV) using a DropPlate 96-S. Based on these measurements, we pooled the DNAs of the same phenotype  equimolarly to create four DNA pools for sequencing  using a NucleoMagB. oleracea TO1000DH3 reference genome v2.1 of EnsemblPlants release 39 . We performed joint genotyping for these contrasts using the GATK CombineGVCFs/GenotypeGVCFs workflow. We then filtered these genotypes further, excluding low coverage per sample < 40×), low coverage across the pair of merged samples (< 100x), unusually high coverage , low values for the GATK Genotype Quality score (< 99), and low values for the frequency of the reference allele . We calculated smoothed G statistics  to estaB2G-FAR, . To deteB2G-FAR, , Fig. 1,Ontology . Within As this developmental process precedes those defined by the other top-level terms in the subgraph, we took , the coverage for this single individual was commensurately higher (approx. 100 × coverage). For this Jersey kale genome, we then used SnpEff  to assesbidopsis . The metThe F1 seeds from our crosses resulted in 42 distinct genotypes that we successfully established in tissue culture and transferred in soil. We found a 420-day time lag between the earliest and the latest flowering F1 genotypes, presumably owing to the heterozygosity of the Jersey kale parent: the first two F1 genotypes  started to flower 113 days after potting, while more than a year later, at day 533, the last F1 flowered (genotype number 17109). The first pool, early flowering EF, comprised 11 F1 genotypes that flowered between 113 and 135 days after potting. The second pool, flowering at intermediate age IF, included eight F1s that flowered from 154–164 days after potting. The third pool, late-flowering LF, represented 11 F1 genotypes that started to flower from 176–239 days after potting. The fourth pool, non-flowering (NF) at the time of DNA extraction (day 294), included 9 F1s that only flowered after DNA extraction, up to 533 days after potting. Phenotyping results are summarized in Fig. 9 and 60.3 × 109 bases, observed in 359.6 × 106 through 399.4 × 106 reads (which are 150 bp on HiSeq X). Given the size of the reference genome (approx. 488 Mbp [FT loci), C7 (spanning a FUL copy) and C8 (spanning a PHYB copy). A comparative view of these Gʹ regions is shown in Fig. Sequencing resulted in yields per pool between 54.3 × 10 488 Mbp ), this cThe term enrichment analyses  yieldeddevelopmental process involved in reproduction). Across the comparisons, this resulted in a consensus subgraph that spanned 14 enriched terms, shown in Fig. Vegetative to reproductive phase transition of meristemGO:0010228—Flower morphogenesisGO:0048439—Floral organ developmentGO:0048437—Floral whorl developmentGO:0048438—Seed maturationGO:0010431—Each of the comparisons enriched a subgraph of the GO topology, which we pruned further to retain only those parts subtended by GO:0003006 . Hence, we then zoomed in on only those genes that are (transitively) annotated with GO:0010228 and used these as input for g:Profiler [Circadian rhythm–plant, , Bo6G099320  and Bo6G120900 .The g:Profiler results show enrichment both for terms above GO:0010228 and those below it. The terms shown with gray background in Fig. 6 raw variants of all types (i.e. including indels and polymorphisms longer than 1 bp). The homology-based SnpEff analysis, which assessed the impact of variants in Arabidopsis flowering time pathway, returned results of comparable magnitude as obtained in previous research in the B. oleracea cultivar ‘Kashirka’ [FT and one of FLC had indels, while the CO copies had non-synonymous SNPs but no indels.Genome sequencing of the Jersey kale yielded 1,092,319,676 forward and reverse reads 150 bp)  there were two lost start codons. One in a TFL1, four copies reside, respectively, on q3, p4, q4, and q9. The copy on q3 has four moderate-impact variants, all of which are non-synonymous SNPs. This copy falls within inferred QTLs for IF–LF and EF–LF. The copy on p4 is unaffected by SNPs and outside any QTLs. The long arm copy on the same chromosome has a moderate-impact non-synonymous SNP and falls within the QTLs for IF–LF and EF–IF. The copy on q9 has a moderate-impact non-synonymous SNP and lies within the QTLs for IF–NF and EF–IF.Of GRP8 (p6), AP1 (q2), and VRN2 (q5). None of these lie inside inferred QTLs. Of GRP8, two more copies are known to reside, respectively, on p1 and p3. The latter copy has two moderate-impact SNPs, namely an in-frame deletion and a non-synonymous SNP, and lies within QTLs inferred for the contrasts IF–NF, IF–LF, and EF–NF.In addition, there were three genes affected by high-impact SNPs having gained stop codons: AP1 there are two more copies, which both reside on q6, within QTLs that were inferred for all contrasts but EF-IF. One copy is unaffected by moderate impact SNPs, while the other has eight non-synonymous SNPs. VRN2 has one additional copy, on q8, which lies within the inferred QTLs for EF–NF and EF–IF. This copy has a moderate-impact non-synonymous SNP. Further, detailed results of the SnpEff analysis are available in Additional file Of n(n-1)/2 working out to six contrasts for four pools). Another complication was the right censoring in waiting time till flowering for pool NF (“non-flowering”): as three plants in this pool flowered after the point of DNA extraction while others never did, the pool is a mixture of very late flowering and non-flowering genotypes. Nevertheless, our results showed consistency across all comparisons in the discovered QTL regions and the GO terms the genes in these regions enriched.Our analysis was somewhat complicated by the proliferation of flowering time cohorts  and the commensurate increase in pool contrasts to consider  and then zoomed in further on nodes subtended by its descendant GO:0010228 (vegetative to reproductive phase transition of meristem). This progression was discovered from the data and should therefore be transferrable to other systems without prior knowledge of the underlying genetics or annotations. As a result of this ontology-mediated approach, we reduced the number of candidate genes from 10,469 to a final set of 29 genes resulting from the g:Profiler analysis. Considering that these genes include those previously established as key in regulating flowering time , we vieOur sequencing of the genome of a Jersey kale accession and subsequent homology-based analysis of SNP impact confirms and strengthens the rest of our findings. The pattern in the impact assessment is that copies of genes involved in flowering time regulation that are affected by high-impact SNPs (lost start or gained stop codons) lie outside of the inferred QTLs, and so gene inactivation through lost start or added stop codons does not modulate flowering time differences between our pools. Conversely, other copies of these same genes that have moderate-impact, non-synonymous SNPs do occur within the QTLs. Because inactivated gene copies lie outside of the QTLs while functionally divergent copies (with reference to TO1000DH3) lie within them we infer that the QTLs are indeed ‘where the action is’ in modulating flowering time through the additive effects of non-synonymous SNPs.Arabidopsis. Another potential weakness lies in the non-parametric Gʹ statistic that we used here. Greater power may be attained using a parametric approach such as GWAlpha [A potential weakness is that the ontology-mediated technique’s usefulness hinges on the quality of genome annotations and KEGG pathways: without the combination of good functional characterization (often homology-based) and a known background against which to perform the hypergeometric tests, gene set enrichment analyses cannot work. In practice, this means that such ontology-mediated techniques will be most applicable to well-studied model organisms or reasonably close relatives of  GWAlpha . HoweverB. oleracea cultivars was purely homology-based, using the pathway in Arabidopsis as the backbone on which to map participating gene copies and their variants [Previous research in flowering time in variants , making B. oleracea model TO1000DH3 phenotyped on flowering time. The data we collected consisted of high throughput sequencing reads, which we analyzed using standard tools for identifying QTLs in pairwise BSA comparisons. This resulted in numerous regions throughout the genome, though concentrated at loci known from previous homology-based research in flowering time regulation and consistent across pairwise comparisons. To reduce the set of candidate loci to more manageable dimensions, we developed an ontology-mediated approach that limits the result set by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. This reduced the resulting gene set from tens of thousands to dozens of candidate genes. A further enrichment test led to the pathway for circadian rhythm in plants. The genes that enriched this pathway are attested from previous research as being involved in regulating flowering time, and some of these genes were also identified as having functionally significant variation compared to Arabidopsis. As such, we validated and confirmed our ontology-mediated results through a more targeted, homology-based approach. However, the ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery. We view our method as potentially applicable to the study of other complex traits and therefore make our workflows available as open-source code and a reusable Docker container. This container is available from the ‘Docker hub’ and can consequently be deployed and applied to user data using the standard docker toolchain, for example as ‘docker run -it -v $DATA:/home/ubuntu/data naturalis/brassica-snps’, where the argument $DATA refers to the user data location. More instructions for this are available at https://hub.docker.com/r/naturalis/brassica-snps.We performed a bulk segregant analysis (BSA) across four cohorts of a cross between the Jersey kale and the Additional file 1: Table S1 Flowering time per specimen, data for Figure 1.Additional file 2: Table S2 Raw yields of bulk sequencingAdditional file 3: Table S3 Results of the SEACompare analysis.Additional file 4: Table S4 Results of the SnpEff analysis.Additional file 5: Table S5 Results of Jersey Kale genome sequencing.Additional file 6: Methods.


Phaseolus vulgaris L.) cultivars. Two populations of snap bean were used in this study. The first population was the BeanCAP (Coordinated Agriculture Project) Snap Bean Diversity Panel (SBDP) (n = 136), and the second population was the Snap Bean Association Panel (SnAP) (n = 378). SBDP was evaluated for white mold reaction in the field in 2012 and 2013, and SnAP was screened in a greenhouse only using the seedling straw test in 2016. Two reference genomes representing the Andean and Middle American centers of domestication were utilized to align the genotyping-by-sequencing (GBS) data. A GWAS was performed using FarmCPU with one principal component after comparing five models. Thirty-four single-nucleotide polymorphisms (SNPs) significantly associated with white mold resistance were detected. Eleven significant SNPs were identified by the seedling straw test, and 23 significant SNPs were identified by field data. Fifteen SNPs were identified within a 100 kb window containing pentatricopeptide repeat (PPR)-encoding genes, and eleven were close to leucine-rich repeat (LRR)-encoding genes, suggesting that these two classes are of outsized importance for snap bean resistance to white mold.White mold can result in snap bean yield losses of 90 to 100% when field conditions favor the pathogen. A genome-wide association study (GWAS) was conducted to detect loci significantly associated with white mold resistance in a panel of snap bean ( Sclerotinia sclerotiorum (Lib.) de Bary, is considered one of the most economically important diseases affecting common bean (Phaseolus vulgaris L.) in temperate growing environments [Coniothyrium minitans or Trichoderma spp. [White mold, caused by ronments . White mrma spp. ,3. Howevrma spp. .The genetic control of resistance to white mold can be partitioned into physiological resistance and disease avoidance . AvoidanMany white mold resistance and avoidance quantitative trait loci (QTL) have been reported for common bean, most with a small to moderate effect and residing in all linkage groups except Pv10 . PreviouGWAS is a powerful genomics tool developed to detect the genes responsible for traits of interest in humans, animals, and plants . SeveralGWAS models must typically account for population structure to avoid a bias for false positives derived from population structure. Population structure in common bean is primarily determined by two regions of domestication: the Middle American center and the Andean center . ReproduThe objective of this research is to better understand the genetic basis of white mold resistance in snap beans by identifying QTL derived from (1) phenotypes from both a greenhouse setting and a field setting (2) using both a Middle American and Andean reference genome to assess novel QTL that may be present in only one reference genome. Snap beans have undergone selective pressures and extensive inter-gene pool introgression distinct from dry beans; the identification of these QTL will facilitate marker-assisted breeding in snap beans for white mold resistance, providing new sources of resistance for dry beans.Two populations of snap bean were used in this study. The Bean CAP (Coordinated Agriculture Project) Snap Bean Diversity Panel (SBDP)  includesField evaluations were carried out at the OSU Vegetable Research Farm located near Corvallis, Oregon, on the lower bench of the Willamette River floodplain on a Chehalis silty clay loam soil at latitude 44.572722, longitude −123.241351 at 77 MASL. Most fields on the farm have a history of moderate to high risk of disease when snap beans are grown, and soils carry a significant load of white mold sclerotia when environmental conditions favor the disease.Seeds were planted at a depth of approximately 5 cm in single-row plots with a length of 3.0 m, 77 cm between rows, and a density of 50 seeds per plot. A single row of bush blue lake cultivar OSU 5630, a white-mold-susceptible check, was planted as borders and end plots (1.5 m) to minimize edge effects. The trials were conducted in fields with a history of disease to ensure natural inoculation during the growing season. Plots were irrigated on a weekly basis with approximately 2.5 cm applied through solid-set overhead sprinklers. At flowering, irrigation was shifted to daily application in late afternoon or early evening for ~30 min to increase leaf wetness duration and create a more favorable microenvironment for disease development.Incidence and severity were measured in three replicates arranged in a randomized complete block design. Incidence was measured as percentage of diseased plants within each plot. Severity was measured by observing diseased plants and rating symptoms using a 1–9 scale as follows: 1 = no disease symptoms on main stem, branches, or pods; 2 = lesions starting on main stem, branch, and/or pods; lesion < 2 cm located in internodal regions; 3 = lesion reaching a node; 4 = lesion passing through a node and extending approximately 2 to 3 cm; 5 = lesion spanning two nodes; 6 = lesion passing through two nodes; 7 = lesion passing through three nodes; 8 = lesion reaching four nodes; and 9 = lesion passing through four nodes or more (or whole-plant collapse). This scoring method was modified in year two to score incidence based on any sign of disease and to score the most diseased plant in the plot for severity. A disease severity index (DSI) was calculated as the geometric mean of incidence and severity for a given year . An averThe SnAP was evaluated for white mold reaction in the greenhouse using a modified straw test  designatThe original SnAP population was genotyped by sequencing (GBS) at the Cornell University Institute of Biotechnology Genomic Diversity Facility . A totalAll data sets were tested for normality with a Shapiro–Wilk test in the R statistical software environment, version 4.2.1. The average severity for 2012 and 2013 and the average of incidence for 2012 and 2013 were normally distributed . The indAnalysis of variance (ANOVA) implemented in R ver. 3.3.2 was conducted for phenotypic data for both field and greenhouse tests. The linear additive model for the field test consisted of lines (genotype) and years as main effects, as well as the interaction of lines with years and replication nested within years. The model for the seedling straw test consisted of lines and replications as main effects.To examine relationships among mean incidence, mean severity, the yearly index, and the greenhouse seedling straw test, Pearson’s correlation coefficients were calculated using PROC CORR in SAS . Least square means were used for all traits as an input into geometric means and two-year averages.2 values through a one-way ANOVA conducted using the SAS GLM procedure (SAS v9.4) with trait as the dependent variable.Five models were tested utilizing the GAPIT software package, version 3, in the R statistical software environment, version 4.2.1: a mixed linear model (MLM), multiple loci mixed linear model (MLMM), Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Fixed and random model Circulating Probability Unification (FarmCPU). One, two, and three principal components (PC) were calculated in TASSEL, version 5.2.84, and tested with each of these models. Based on a visual evaluation of Q-Q plots for the best fit around the null distribution, FarmCPU with one PC axis was determined to be the best model, and all GWAS analyses were performed with this model. FarmCPU is an R package developed by Liu et al. (2016)  that expp < 0.01). Highly significant differences were also observed among replicates (p < 0.01) (2) and coefficient of variation (CV) were 0.57 and 24.99, respectively.An ANOVA of the seedling straw test applied to the SnAP in the greenhouse for disease severity showed that differences among lines were highly significant ( < 0.01) . The regLeast square means (LSM) for disease severity in the straw test showed a wide range of responses to fungal inoculation from the extremely susceptible to extremely resistant . The LSM2 according to one-way ANOVA for this SNP was a considerable 0.34. The smallest effect size was associated with the G19833 genome on Pv03 at position 46,795,664, with an effect of −0.03 on a log scale that back transforms to 0.97 on a nine-point scale. This SNP had an R2 of 0.01. The combined R2 value for all significant SNPs found using the G19833 genome was 0.59, and the combined R2 value for all significant SNPs found using the 5-593 genome was 0.49.Eleven SNPs distributed across six linkage groups  showed significant association with white mold resistance . The mosA candidate gene search within 100 kb of each SNP position yielded one to three plant-defense-related genes per position . Three op < 0.001) for incidence, severity, and DSI (p < 0.001) for all traits, whereas variety x replication showed no significant differences any of the independent variables. Incidence, severity, and DSI had relatively high R2 values, with the model accounting for 89, 79, and 85% of the observed experimental variation, respectively. The coefficients of variation (CVs) for incidence, severity, and DSI were 54.4, 43.1, and 50.2, respectively.Main effects  were highly significant , indicating similarity in ranking for these two disease parameters . As woulAn association study conducted on the SBDP for incidence, severity, and DSI identified total of 26 SNP positions, of which 3 were duplicate SNP positions identified in different data sets, for a total of 23 non-duplicated SNP positions .p-value was the SNP on Pv05 at position 38,076,792, the G19833 genome, which was also detected when utilizing the combined-years incidence data set and the DSI 2012 data set. However, this SNP did not have the highest R2 value as determined in a one-way ANOVA, although the R2 value was comparatively high, at 0.14. The highest R2 value belonged to the SNP found with the 5-593 genome on Pv08 at position 10,162,589, with an R2 value of 0.20. The same SNP on Pv08 was also detected with the DSI 2012 data set. The SNP found with the 5-593 genome on Pv05 at position 43,365,299 also had a relatively high R2 of 0.13. The remaining three SNPs had relatively low R2 values of 0.02, and one SNP had statistically meaningless R2 of <0.01 when analyzed by one-way ANOVA. The combined R2 for G19833 was 0.16, and the combined R2 for 5-593 was 0.35 in the combined-years severity GWAS.Of the 26 SNPs detected in the field GWAS studies, six were found using the combined-years severity data set. Two of these six resulted from applying the G19833 genome to the data set, and four SNPs resulted from applying the 5-593 genome ; Table 52 was slightly lower than the combined-years severity data, at 0.09. The highest R2 value belonged to the SNP on Pv08 at position 9,597,483, with an R2 value of 0.21. The SNP on Pv03 at position 6,274,693, 5-593 also had a relatively high R2 of 0.10. Three additional SNPs had R2 values ranging from 0.04 to 0.07, and a fourth SNP was a statistically meaningless (<0.01) according to the one-way ANOVA. The combined R2 for G19833 was 0.37, and the combined R2 for 5-593 was 0.20.Of the SNPs for incidence detected in the field GWAS, seven were found with the combined-years incidence data set. Three of these resulted from applying the G19833 genome to the data set, and four resulted from applying the 5-593 genome ; Table 6p-value was on Pv06 at position 35,297,840, 5-593, and a second SNP on Pv04 at position 38,778,892, G19833, was also highly significant. Despite their low p-values, these two SNPs had R2 values of only 0.05 and 0.01, respectively. The largest R2 value at 0.23 belonged to an SNP on Pv08 at position 10,162,589, 5-593, that is also found in the results of the combined-years severity data set. The SNP on Pv05 at position 38,076,792, G19833, also had a high R2 value of 0.10. This SNP on Pv05 is the same SNP that was identified as significant by the combined-years severity and combined-years incidence data sets. The remaining SNPs had R2 values ranging from 0.1 to 0.5, with a single SNP at the statistically insignificant value of <0.01. The combined R2 value for SNPs detected with the G19833 genome was 0.17, and the combined R2 for those detected with 5-593 was 0.33.Of the SNPs detected in the field GWAS, nine were found with the DSI 2012 data set. Of these, four were found using the G19833 genome, and five were found with the 5-593 genome ; Table 72 was also relatively high, at 0.12. The highest R2 of 0.20 was associated with an SNP on Pv03 at position 430,964 identified within the G19833 genome. The SNP on Pv03 at position 357,403, 5-593, had a similar R2 of 0.19, and it is likely that these SNPs are identical QTL found in different reference genomes. The last SNP on Pv02 at position 41,768,892, G19833, had an R2 of 0.12, and is also likely a parallel QTL found in both reference genomes at roughly the same genomic position. Further corroborating the notion that these are the same QTL in different reference genomes is the identical gene annotation found at each SNP position in each reference genome. The SNP on Pv02 occurs within a gene model that is annotated as a T-complex protein 1 subunit (CCT1) in both reference genomes, and the SNP on Pv03 similarly lands within a gene model annotated as an ARM repeat superfamily protein in both reference genomes  MAGIC population did not yield any overlaps with our study, although one QTL identified in the current study was adjacent to one reported in [A recent GWAS study of white mold resistance in a dry bean  located on six chromosomes . None ofIn addition to these GWAS studies and an unpublished M.S. thesis , genetic2 values were obtained in this study. This is to be expected for a quantitative trait with numerous contributing loci. This can be extrapolated further through genomic prediction utilizing all SNPs for model construction and selection, even if any individual SNP has an extremely small effect. This is a modeling approach that was not used in the present study, although some SNPs with very small effects were captured through GWAS. Future studies should examine the utility of genomic prediction compared to GWAS.A mix of SNPs ranging from low to relatively high SNP effects and RRalstonia infection revealed a differential upregulation of PPRs upon infection [A high preponderance of PPR genes in close proximity to associated SNPs for white mold resistance has not been noted in previous studies. PPRs are not R genes but are recognized as disease resistance gene analogs . PPRs innfection . Constitnfection . Our resnfection ,37,38. Lnfection . A quartnfection ,39,40. NThe frequency of PPR genes identified in this study is not related to the frequency of this gene family in the genome. A keyword search of the G19833 genome, v2.1, reveals 514 copies of PPR, and a keyword search of the 5-593 genome, v1.1, yields 502 copies. At a total length of 537,218,636 bp, the G19833v2.1 genome would be expected to contain one PPR copy per 1.04 Mb. Likewise, the 5-593v1.0 genome is 572,228,222 bp in length, with one copy expected every 1.14 Mb. Our window for a candidate gene search was far smaller, at 200 kb, which should only yield 6 to 7 copies of the PPR gene if the average distribution were applied to our search windows; however, we found 15 copies near our associated SNPs.Fusarium infection upregulated ankyrin gene expression [Arabidopsis [Brassica napus infected with white mold showed induced ethylene pathway activity based on an analysis of transcriptome activity 48 h post infection [Other defense-related genes were identified in this study. Two ankyrin genes were identified near associated SNPs. Ankyrin is known to be involved in PAMP immune recognition . A studypression . A gain-bidopsis . Tandem bidopsis ,44. Ethynfection ,45. In anfection ,59,60,61fin, which controls determinacy in common bean. Determinate growth habit has been associated with white mold avoidance, and we screened both determinate and indeterminate accessions in the greenhouse straw test in which this SNP was observed. However, we did not expect the greenhouse straw test to correlate with variation in avoidance traits. Finally, some SNPs might be associated with regulatory genes, and we did not search for these for similar reasons.There may be additional candidate genes not identified in this study. Some SNPs may be associated with avoidance traits, and we did not attempt to identify such candidates because avoidance constitutes is a very broad set of traits that are pleiotropically influenced by many genes. The SNP at 46,254,411 on Pv01 is near In this study, we identified 34 SNPs associated with white mold resistance from a broad snap bean panel. We used both greenhouse straw tests and field assessments of white mold resistance. Our GWAS models were run with reference genomes from both an Andean and Middle American background, and unique SNPs were identified in each of these, demonstrating the utility of using different reference genomes to tease out associated SNPs that may not be present in some genetic backgrounds. The resulting SNPs were within 100 kb on either side of defense-related genes. The genes for pentatricopeptide repeats and leucine-rich repeats were particularly enriched and may represent an important research avenue with respect to quantitative resistance to white mold.


NAC048 and this change makes a missense variant in only 33.3% of the accessions with a large PR, compared with the accessions with a short PR length. Moreover, we detected five more SNPs in this gene and in NAC3 that allow us to discover closely related accessions according to the phylogenetic tree analysis. We also found that the association between genetic variants among the 18 genes with the highest scores in our GWAS and the phenotypic classes into which we divided our accessions are not straightforward and likely follow historical patterns.Genome-wide association studies (GWAS) have allowed the identification of different loci associated with primary root (PR) growth, and Arabidopsis is an excellent model for these studies. The PR length is controlled by cell proliferation, elongation, and differentiation; however, the specific contribution of proliferation and differentiation in the control of PR growth is still poorly studied. To this end, we analyzed 124 accessions and used a GWAS approach to identify potential causal genomic regions related to four traits: PR length, growth rate, cell proliferation and cell differentiation. Twenty-three genes and five statistically significant SNPs were identified. The SNP with the highest score mapped to the fifth exon of  Arabidopsis thaliana (from now on Arabidopsis) has been widely used for GWAS due to the availability of many sequenced natural isogenic lines . The significance of SNP associations was determined at the 5% FDR threshold computed by the Benjamini–Hochberg–Yekutieli method to correct for multiple testing [https://aragwas.1001genomes.org/ (accessed on 24 June 2022) and TAIR10 (www.arabidopsis.org/ (accessed on 24 June 2022).Genome-wide association mapping was performed using the GWAPP web interface, which contains genotypic information for −206,000 SNP markers  (http://) ≥ 0.05 . These p testing . For eachttp://signal.salk.edu/atg1001/3.0/gebrowser.php (accessed on 24 June 2022) were used to analyze polymorphism among accessions with contrasting phenotypes . SNPs located in the 10-kb window (5 kb up and downstream) of the associated SNPs were mapped and examined for natural variation [https://plants.ensembl.org/Arabidopsis_thaliana/Tools/VEP (accessed on 12 July 2022). Those SNPs found in the greatest number of significant accessions for each trait were counted to find SNPs with biological relevance.Sequence data from the 1001 genome project  (http://ariation . SNPs inariation ) was exehttp://signal.salk.edu/atg1001/3.0/gebrowser.php, accessed on 2 August 2022) and removed incorrect symbols in sequences to maintain an appropriate FASTA file for each gene. A total of 1368 nucleotide sequences (18 genes × 76 homologs) were analyzed. FASTA files for each of the 18 genes were aligned in MAFFT software  using the default settings with 1.53 of gaps opening penalty [We extracted the genomic sequence of 18 genes with the highest scores in the GWAS analyses  [https://itol.embl.de/, accessed on 15 August 2022) to visualize and include the branch colors and tags of the country of origin [Phylogenetic analyses were performed with IQ-tree software (st 2022) ,53. Firsf origin .Single nucleotide polymorphisms were extracted with a tool set Adegenet 1.3.1, R package  31 Augu. Our anahttp://bar.utoronto.ca/eplant/ (accessed on 24 June 2022)).The in silico gene expression analysis in the roots was visualized using the ePlant browser of Araport, Tissue-Specific Root eFP (NAC048 and NAC3, display different SNPs in their exons in contrasting accessions of PR length and, in some cases, these accessions group together, according to our phylogenetic analysis. These genes can be good candidates to validate their function in PR growth through loss or gain of function mutants.In this work, we use a combined methodology of GWAS and phylogenetic gene tree reconstruction in order to search for candidate genes and further analyze relevant genetic variants that participate in PR development. The GWAS allowed us to identify 23 genes that could participate in the control of proliferation and elongation in the PR. However, the alignments of six of these genes and the reconstruction of phylogenetic trees did not show robust patterns in genotype–phenotype associations. Interestingly, two members of the NAC family,


As the fourth most populous country in the world, Indonesia must increase the annual rice production rate to achieve national food security by 2050. One possible solution comes from the nanoscopic level: a genetic variant called Single Nucleotide Polymorphism (SNP), which can express significant yield-associated genes. The prior benchmark of this study utilized a statistical genetics model where no SNP position information and attention mechanism were involved. Hence, we developed a novel deep polygenic neural network, named the NucleoNet model, to address these obstacles. The NucleoNets were constructed with the combination of prominent components that include positional SNP encoding, the context vector, wide models, Elastic Net, and Shannon’s entropy loss. This polygenic modeling obtained up to 2.779 of Mean Squared Error (MSE) with 47.156% of Symmetric Mean Absolute Percentage Error (SMAPE), while revealing 15 new important SNPs. Furthermore, the NucleoNets reduced the MSE score up to 32.28% compared to the Ordinary Least Squares (OLS) model. Through the ablation study, we learned that the combination of Xavier distribution for weights initialization and Normal distribution for biases initialization sparked more various important SNPs throughout 12 chromosomes. Our findings confirmed that the NucleoNet model was successfully outperformed the OLS model and identified important SNPs to Indonesian rice yields. Rice, as a staple food for over half of the worldwide population, becomes an ideal species model within the monocots plant genomic research community9 due to its genome’s smallest size , relative simplicity and completeness, dense map, and also ease of manipulation10. Recall that the Food and Agricultural Organization of the United Nations estimated that by 2050 the worldwide population will increase 32% to 9.1 billion11. Particularly, Indonesia had a 1.09% increase in population growth rate by 202013 and thus has to increase the annual rice production to feed its entire population and achieve national food security.Yield is one of the superior rice traits which is controlled by multiple genes . Through a Genome-wide Association Study (GWAS), its genetic makeups can be discovered and perceivedindica and japonica subspecies genome sequences database15 in many former studies manifests a remarkable improvement to break the conundrum of identifying what genes influence such traits. By delving deeper to the nanoscopic level, Single Nucleotide Polymorphism (SNP) has been widely applied to predict plant traits23. In recent years, the yield prediction-related tasks for rice genomic data have been completed using statistical genetic models to machine learning-based open frameworks26.GWAS that has been deployed for 32. In Indonesia, a Genetic Generalized Double Pareto Regression (GGDPR)6 model incorporates the 1232 Indonesian rice SNPs from 467 accessions with two field indicators and plant varieties as confounding variables. The same dataset is used for this research. GGDPR could control the covariate and allow the repeated measurements for the same rice species in a distinct environment. The algorithm itself, through its shrinkage prior ability, was claimed to successfully handle a condition where the number of the predictors 34, as usually happens in GWAS. With a 0.3% of false discovery rate, GGDPR revealed nine significant SNPs to Indonesian rice yields. One of the SNPs, TBGI050092  resides within a gene responsible for rice growth36. Another intronic SNP, id10003620  produces a pentatricopeptide protein, which plays role in stress and developmental response in rice37. Meanwhile, the protein product of TBGI272457  equipped rice plants with pathogenic resistance39. This study uncovers more important SNPs to Indonesian rice yields by constructing a novel deep polygenic neural network model, named the NucleoNets.Rice yield predictive models should consider confounding variables40 was involved in the NucleoNets. Third, we kept covariates  in the NucleoNet’s wide model compartement41 as proportional memorization against the primary deep model. Fourth, the ablation study was conducted to witness the impact of different parameters initialization against the SNP importance results. Lastly, as the AI-based polygenic modeling for GWAS was completed, we revealed 15 novel important yield-associated SNPs through the NucleoNet’s attention mechanism42. Our research offers the availability of the new state-of-the-art with deep learning methods as a stepping-stone to answer the problem of crop yield predictions.In this paper, we present several contributions as follows. First, we designed NucleoNets as the first Artificial Intelligence (AI) based predictive model for the Indonesian rice genomics data. Second, since SNP is scattered in chromosomes with a distinct position index, the learnable SNP positional embeddingThe research problem comprises the development of a deep polygenic neural network to predict Indonesian rice yields and reveal new important yield-associated SNPs. The developed hypothesis is that the Indonesian rice yields prediction performance of the NucleoNet model can outperform the basic linear regression model, i.e. Ordinary Least Squares (OLS) and OLS with an Elastic Net (ENET). To achieve these goals, there are five phases of the methodology.First, both phenotype and genotype datasets were preprocessed. Second, basic regression modeling was developed to assess the dataset feasibility. Regression is also required for comparison, which is much more commonly used in GWAS. Third, the NucleoNet model was constructed, inspired by the Wide and Deep model. Next, the evaluation phase was done with various metrics to measure the model performance. Lastly, the t-test was conducted to test the hypothesis.The dataset used for this research was originally curated by the Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development (ICABIOGRAD). The database collection consists of 467 rice germplasm samples, 467 × 1536 genotypes (SNPs), and 467 × 4 locations × 12 phenotypes. In detail, the germplasm sample consists of 136 local varieties, 162 improved lines, 11 wild species, 34 near-isogenic lines, 29 released varieties, and 95 newly identified varieties. These samples contain 77 Japonica, 108 Tropical Japonica, and 249 Indica subspecies, leaving the remaining 33 samples with unlabelled subspecies. The Indonesian rice genome consists of 12 chromosomes, which each has different numbers of SNP. The proportion is depicted in Fig. chr), SNP ID (snp), SNP position in DNA sequence (pos), reference allele (ref), alternative or mutated allele , and genotype data/SNP (gt) itself. Meanwhile, the phenotype file describes 12 available rice traits  and found that only 57 Indonesian rice SNPs (4.63%) were registered in the International Rice Research Institute (IRRI) database (see Table This preprocessing phase aims to create a Genotype–Phenotype (GP) table consisting of the following columns: sample ID, sample name, sample location, sample variety, SNP, SNP position, and yield. Note that samples from the Greenhouse were excluded since all yields are unreported  software for SNPs with call rate beyond 25% and removal of monomorphic SNP, 697 rice samples × 1232 SNPs were obtained. The alternative imputation services are Online Plant-ImputeDB or Rice Imputation Server44 which utilized cloud computational offloading technology45. Note that before the imputation, referring to the raw data we received, the call rate of 9 significant SNPs is 0.222% for TBGI036687, 1.774% for TBGI050092, 0.665% for id4009920, 1.109% for id5014338, 1.330% for both TBGI272457 and id8000244, 20.843% for id7002427, 2.217% for id10003620, and 0% for id12006560. The call rate is calculated by dividing the number of samples that have a null value in their related SNP by the total number of samples.The previous workNext, from the 697 samples, mild and extreme outliers in the yield data were detected by using the Interquartile Range (IQR) method. From here 10 missing yields were dropped and the outliers were imputed with the global mean. Therefore, the final Genotype–Phenotype table has 687 rice samples, with each has 1232 SNPs (genotypes) and 1 yield rate (phenotype to predict). See Fig. 46. The scheme encodes SNP according to the total of its alternative allele, as it represents a mutation in one locus .Note that in the genotype dataset, all SNPs were encoded based on the additive model6 were gathered and compared. These SNPs were then retrained in the OLS model to seek the best prediction score against the rice yield. The trial was also intended to meticulously examine whether there are beneficial insights and impacts of using only the partial SNP data.The GP Table data frame was shuffled and 85% of the total data was then reserved for train data. After this splitting, the t rain data has a coefficient of variation (CV) of 1.878, and the test data has a CV of 1.798, which still showed the fair dispersion of yield data. In this regression section, we rendered three experiments. First, all SNPs were included in the Ordinary Least Squares (OLS) as a part of polygenic modeling (Experiment 1). Second, each SNP was regressed to yield as a part of an independent association test or marginal regression (Experiment 2), as commonly found when dealing with GWAS. Third, the Elastic Net (ENET) regression was conducted to see the results under the coefficients penalty (Experiment 3). All SNPs were included when the ENET was performed. Its results were plotted into the correlation heatmap to scrutinize the effects of the alpha constant  and L1 ratio tuning. This ratio works by 0 < L1 ratio < 1. Both alpha and L1 ratio spaces follow the arithmetic sequence of 49. Given a search space, Hyperopt returned the best hyperparameters for the model, and hence the validation accuracy can be optimal50.The GP table was loaded and shuffled. A tensor object was then created for SNP data  prediction57, pharmacogenetics58, and even the epistasis analysis59. Equation  loss function . Experiment 2 is the same except there is an additional modified ENET penalty in the loss function . Note that both ENET and Generalized Double Pareto (GDP) which was implemented in previous research55 in the loss function . This entropy acts as a control for the dispersion of attention scores across all SNPs. In other words, we prevent the attention score from collapsing to only one SNP. Equation  in terms of weight initialization were also conducted, as summarized in Table 6 where it was considered Inspired from the previous study61. See the Supplementary Information about the selection reason for these metrics. Note that due to the nonlinearity of the dataset, the Coefficient of Determination or R-squared  was performed for hypothesis testing.Due to the prediction task, the best possible way to measure the model performance on the test dataset is by using ® CoreTM i5-8250U @1.60 GHz (8 CPUs) ~ 1.8 GHz processor, X442UQR/X442UQR.308 system model, 16,384 MB RAM, and Windows 10 (64-bit) operating system. Developer software includes Jupyter Notebook 6.0.1, Rstudio 1.1.463, Preferred Installer Program/PIP 21.2.4, and PLINK 1.9. The main programming language is Python 3.7.1. Python libraries used are Torch 1.9.0, Pandas 1.3.3, Scikit-allel 1.3.5, Scikit-learn 0.24.2, Hyperopt 0.2.5, Statsmodels 0.12.2, Statistics 1.0.3.5, Matplotlib 3.4.3, Seaborn 0.11.2, and Numpy 1.19.5. All libraries may have the alternative and can be installed through the Python package manager .The research was executed in hardware with specifications of Intel6, the total data used is 697 samples. All 10 missing yields from Citayam were dropped, leaving 687 samples. The outliers were detected using the Interquartile Range (IQR) method, with Lower Outer Fence (LOF) of − 6.38, Lower Inner Fence (LIF) of − 2.19, Upper Inner Fence (UIF) of 8.98, and Upper Outer Fence (UOF) of 13.17. Precisely, 27 mild outliers were appeared and then imputed by 3.449 as the global mean of rice yield. No extreme outlier was found.The same 467 species were grown in three distinct locations, i.e., Kuningan (2010–2011), Subang (2011–2012), and Citayam (2012–2013). Referred from the previous researchAs we plotted the density distribution of rice yields in each location, 150 samples from Kuningan (5.01 ± 1.98) has the Skewness coefficient From the OLS, which is part of Experiment 1, we obtained 16 significant SNPs. From Experiment 2, where we regressed each SNP to yield, we obtained 36 significant SNPs. See the results in Table In Experiment 1, we performed 7 ablation studies (ABSTs) with distinct weights and biases initialization. Each of the ABSTs used hyperparameters found by Hyperopt, as inscribes in Table Experiment 2 was run in 1000 epochs with approximately 5000 s of ET. The validation scheme for NucleoNetV2 obtained an MSE of 3.097 and consumed about 1 h 16 min of ET. Referring to the MSE measurement, NucleoNetV2 gave testing scores of 2.782, 2.799, and 3.035 for ABST-3, ABST-6, and ABST-7. See Table In Experiment 3, we only reported the NucleoNetV3 testing results on ABST-6 since the SNP importance occurrence variation in the Manhattan plot is much higher than ABST-3 or ABST-7. The validation scheme for NucleoNetV3 obtained an MSE of 3.233 and consumed about 1 h 35 min of ET. NucleoNetV3 gave an MSE of 2.863, trained within 1,000 epochs and consumed approximately 4900 s of ET. See Table In addition, to compare with other deep neural network model and to show the advantage of the NucleoNets, wide and deep model was trained with the same hyperparameters setting of NucleoNetV3. As shown in Table The use of seed = 43 is to let this experiment reproducible. However, Fig. We presented the performance comparison between the GGDPR model, polygenic OLS regression models, and deep polygenic NucleoNet models, as shown in Table 6, namely TBGI272457  which are involved in pathogen recognition and activation of fundamental and innate plant immune system71. The presence of these genes brings disease resistance capabilities in rice72 and hence supports the sustainability of rice yields.Let the NucleoNet To the day this research is written, there is no prior use of attention score as a fundamental threshold to select important SNPs like 73. Its existence was also proved to protect rice seed germination74. Its enzyme product, β-Glucosidase, has an impact on the rice root76. TBGI272488 was discovered as a rice yield-associated gene77. The SNP also controls the ATP-binding cassette (ABC) transporters80 which contributes to multidrug resistance in plants, including rice82. TBGI336599 was reported to have an impact on rice growth83. TBGI130922 controls the metabolism, including the cytokinin metabolism75, to support rice coleoptile growth84. One product of this gene is flavonoid-biosynthesis networks86. These flavonoid compounds have many roles in plants, including the reproduction process87 and specialized metabolite pathways88 in rice. The rest of the SNPs have no further description since they have not been mapped in the rice DNA strand. The other reason is their protein products are still hypothetical. Please refer to Tables Based on this threshold, we summarized the top five important SNPs found by each NucleoNet model, as shown in Table The hypothesis testing (known as NHST) was performed using 38 out of 104 testing data, and thus the degree of freedom is 37. The rest data were excluded due to data distinctions at the time of shuffling the test data for OLS and NucleoNet models. The population to be tested is squared error results from NucleoNetV1  The Indonesian rice yields prediction performance of NucleoNetV1, NucleoNetV2, and NucleoNetV3 outperformed the OLS model. (2) The Indonesian rice yields prediction performance of NucleoNetV1, NucleoNetV2, and NucleoNetV3 has no difference with the OLS + ENET model. (3) Additional entropy penalty in the NucleoNet model brought a more diverse distribution of attention score across SNPs, at the expense of prediction accuracy as a cost. (4) Ablation study showed that the combination of Xavier distribution for weights initialization and Normal distribution for biases initialization sparked more various important SNPs. (5) Two significant SNPs discovered in the prior research, TBGI272457 and id4009920, were also discovered using the NucleoNets.Since this research is still in its early stages, our future works in the Indonesian rice genomics field will focus on the following things: (1) Extend the covariates, including the influence of pests, pesticides, and climatic information in the year where the rice was planted. (2) Develop a particular deep learning model to impute missing SNPs. (3) Try various attention mechanisms such as self-attention or multi-head attention to improve the SNP significance measurement. (4) Implement the Deep Learning Important Features (DeepLIFT) model to handle SNP significance. (5) Reinforce the deep learning model by instilling it with a novel inductive bias for genomics data. (6) Compare deep learning results with broader common GWAS methods such as LASSO or Bayesian approaches. (7) Develop a biological-based method to validate that important SNPs found in the NucleoNets are useful to increase the annual rice production rate.Supplementary Information.


Ae. tauschii introgression underlying the most significant of these associations and extracted the introgressed Ae. tauschii genes, revealing candidates for further dissection. Incorporating these exotic alleles into breeding programmes could serve as a pre-emptive strategy to produce high yielding wheat cultivars that are resilient to the effects of future climatic uncertainty.Global warming poses a major threat to food security and necessitates the development of crop varieties that are resilient to future climatic instability. By evaluating 149 spring wheat lines in the field under yield potential and heat stressed conditions, we demonstrate how strategic integration of exotic material significantly increases yield under heat stress compared to elite lines, with no significant yield penalty under favourable conditions. Genetic analyses reveal three exotic-derived genetic loci underlying this heat tolerance which together increase yield by over 50% and reduce canopy temperature by approximately 2 °C. We identified an  Comparative phenotyping and genome-wide association study in a panel of 149 wheat genotypes reveal alleles associated with heat tolerance, which could be useful in future breeding programmes for wheat cultivars. Heat stress is one of the major abiotic stressors that impacts global wheat production, reducing leaf area, crop duration and the efficiency of photosynthesis and respiration3 as well as reducing floret fertility and individual grain weight4. Together, these physiological consequences negatively impact productivity3 with potential devastating effects. For example, in 2010, Russia saw a 30% reduction in wheat yield during their hottest summer in 130 years5. Cases like this could become commonplace as global warming causes temperatures to rise and extreme weather events to become more frequent. Simulations predict that global yields will fall by on average 6% for each 1 °C increase in temperature6, with some regions reaching 9.1% ± 5.4% per 1 °C rise7. Adaptation to future climate scenarios is vital to ensure global food security8. Climatic instability, combined with environmental constraints, such as restricted supplies of irrigation water and arable land loss, emphasises the need for breeding strategies that deliver both increased yield potential during favourable cycles and resilience to abiotic stress and environmental constraints.Wheat is among the most widely cultivated crops in the world with more than 216 million hectares grown annually9, due to historic genetic bottlenecks11 compounded by intensive artificial selection by breeders12. A strategy employed by the International Maize and Wheat Improvement Center (CIMMYT) to increase the genetic diversity of wheat pre-breeding material is to incorporate exotic parents in their germplasm via strategic crosses13. The most common exotic parents used are Mexican and other origin landraces14 and primary synthetics, which are produced by hybridising tetraploid durum wheat with Aegilops tauschii, the ancestral donor of the D genome, to recreate hexaploid bread wheat15; these synthetic lines act as a bridge to introduce durum and Ae. tauschii variation into modern hexaploid wheat. This approach has been successful in introducing disease resistance as well as drought and heat adaptive traits17. Landrace and synthetic material have been identified with superior biomass in comparison to elite lines under drought and heat conditions19 and elite lines that include landrace or synthetic material in their background have been developed in recent years for drought, heat, and yield potential conditions22.Such adaptation relies on genetic variation underlying the traits of interest; however, modern elite wheat material typically has limited genetic variation, particularly in the D genome23 as breeders tend to avoid exotic materials because of large regions of poor recombination and a fear of linkage drag24. Furthermore, despite evidence of the contribution of exotic material in wheat improvement, the physiological and genetic bases of heat tolerance in this material remain unclear.Challenges remain for the effective deployment of landrace and synthetic material. Only a small fraction of these vast collections of crop genetic resources have been evaluated for climate resilience traits and potential tradeoffs under favourable conditions have not been assessed. Currently, most of these genetic resources are unusedAe. tauschii underlying an MTA and employ in silico mapping downstream of the GWAS to narrow down the interval, explore recombination and identify candidate genes.Here, we evaluate a spring wheat panel in the field containing contrasting material controlled for phenology and plant height under heat stress and yield potential conditions. We explore yield and related physiological traits and compare exotic-derived lines with elite lines. We conduct a genome-wide association study to reveal marker trait associations (MTA) with heat tolerance traits and evaluate their impact under favourable conditions. Finally, we identify introgressed 25. It contains 149 lines, some of which are elite while others contain exotic material from landraces, synthetics, and wild relatives  for two consecutive years under yield potential and heat stressed irrigated conditions in NW-Mexico , but positive correlations were observed between plant height and yield under heat stressed environments . The better performance of exotic-derived lines was validated using the stress susceptibility index (SSI). This measure is negatively correlated with yield under heat stressed conditions; thus, lower SSI values indicate higher tolerance to a stressful environment. Compared to elite lines, exotic-derived lines had significantly lower SSI values for yield, grains per m2 and biomass at physiological maturity, but not for thousand grain weight , a proxy for biomass, and significantly lower canopy temperature during both vegetative and grain filling stages under heat stressed conditions but not under yield potential conditions Fig. . NDVI meTo explore the genetic bases of these exotic-derived heat tolerance traits, marker-trait association analyses were performed using Best Linear Unbiased Estimator (BLUE) means from two or four replicates for each measured trait over two growing seasons. The most relevant MTAs are shown in Supplementary Table Lines with the favourable C allele on 1B and 2B and the unfavourable A allele on 6D have 24.3% higher yield under heat stress; lines which also have the favourable T allele on 6D have 56.5% higher yield under heat stress compared to lines with the three unfavourable alleles Fig. . AssuminAe. tauschii, Th. ponticum or S. cereale and reduced mapping coverage, seen as coverage deviation (mapping coverage compared to the median mapping coverage across the panel) significantly below 1, which indicates breaks in synteny between wheat and the introgressed chromosome segment. Using this approach, we identified introgressed Ae. tauschii material at the beginning of 6D in all 33 lines with the T/T genotype and all 7 lines with the A/T genotype at MTA 6D-6276646, where T is the favourable allele. As Ae. tauschii is from wheat’s primary genome and thus very similar to the D subgenome, not every 1Mbp window is sufficiently lacking in synteny for reads to map poorly and produce significant coverage deviation below 1. This explains why some windows within the introgression have coverage deviation of around 1. However, these windows still have Ae. tauschii-specific SNPs and are within a block of 1Mbp windows in which most have significant coverage deviation below 1. Therefore, we can be confident that the introgression includes these windows.Due to the better performance of exotic-derived lines under heat stress and exotic-derived lines possessing alleles for heat tolerance, we searched for introgressed material overlapping the MTAs. We detected introgressed material in HiBAP I lines by looking for genomic blocks containing windows with SNPs specific to Ae. tauschii. Using chromosome and protein alignments, we anchored this 1.80Mbp core region from the wheat RefSeq v1.0 genome to the Ae. tauschii reference genome, Aet v4.026, and extracted the syntenic 1.49Mbp region between 4.63Mbp and 6.12Mbp. This represents the probable introgressed content of the core introgressed region and likely contains the gene(s) responsible for the MTA  Fig. . The segAe. tauschii genome and a list of genes that had been introgressed ; a mitogen-activated protein kinase (MAPK) gene found in two of the Ae. tauschii accessions with no orthologue in wheat; and a pair of type-B two-component response regulator receiver proteins, orthologous to type-B Arabidopsis response regulators (ARRs) with closest similarity to ARR-11. One member of the pair, AET6Gv20025700, appeared to have a myb-binding domain that is missing from the wheat orthologue gene model. However, after manual reannotation, we found that this difference was a misannotation in wheat so likely not causing a functional difference. We also found that both ARR genes were expressed in spike and grain in both Ae. tauschii and wheat but not in leaf or root, whereas the other candidate genes were expressed across leaf, root, spike and grain tissues. This might exclude the ARR genes for involvement in the heat tolerant phenotype which is established during the vegetative stage and maintained through grain filling. For the 1B and 2B MTAs, as they were not within an introgression, we submitted the sequence 1Mbp up and downstream of the MTA to Knetminer, a gene discovery tool28. Within the 2B interval, we identified DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN 1A (DREB1A) and STEROL GLUCOSYLTRANSFERASE (SGT) as promising candidate genes. The functional evidence of candidacy for each candidate gene is outlined in Supplementary Note 1.For the 6D MTA, we identified the syntenic region in the sed Fig. . As we a29, drought19 and heat stress30. In the present study, exotic-derived lines performed better under heat stress than elite lines with no yield penalty under yield potential conditions. This increased yield under heat stress was associated with a range of factors, including higher biomass throughout the crop cycle, higher grain number and cooler canopy temperature during both vegetative and grain filling stages. Contrary to other studies32 higher pre or post anthesis biomass (NDVI) or lower canopy temperature was not associated with higher yields under favourable conditions. Cooler canopies have been previously associated with higher tolerance to drought and heat irrigated environments33 and with optimised root distribution in bread wheat34. Plants with an optimised root system are able to satisfy the high evaporative demand through elevated transpiration rates under hot irrigated conditions and thus maintain cooler canopies35. Higher transpiration rates are associated with increased stomatal conductance that, in turn, is associated with higher photosynthesis that can explain the higher biomass observed in exotic-derived lines in comparison with elite lines. However, according to temperature response models in wheat6, the observed reduction in plant temperature of approximately 2°C would be unlikely to account alone for the >50% increased yield of exotic lines6.Exotic parents are routinely used to increase genetic diversity in wheat pre-breeding pipelines and their enhanced performance has been demonstrated under salinity36. Therefore, plant height may be influencing the better performance of exotic-derivatives. Among all stress indices, the stress susceptibility index (SSI) is thought to be the most useful index for evaluating tolerant cultivars. Exotic-derived lines had significantly lower SSI than elite lines, adding additional support to the resilience of this exotic material under heat stress.Despite variation in plant height being restricted, exotic-derived lines were taller than elite lines in both environments. Plant height and phenology were restricted under yield potential conditions, but the variation under heat stress environments was not initially considered for the panel construction. Interestingly, lines that performed well under heat stress had the lowest difference in plant height between yield potential and heat stress conditions. Taller plants have better light interception and a better light distribution in comparison with shorter plants, and this has been associated with increased photosynthesis37. Secondly, insensitivity to the photoperiod was confirmed by marker analysis where among the known major adaptation genes, the spring allele at Vrn-B1 (Vrn-B1a) and Vrn-D1 (Vrn-D1a) and the Ppd-insensitive allele at Ppd-D1 (Ppd-D1a) were present in ~90% of the HiBAP I panel38. Finally, delayed sowing at CIMMYT’s Obregon station is routinely used for evaluating heat-tolerant breeding material, and several studies confirm the value of late sowing at this experimental site to develop germplasm adapted to different heat stressed environments worldwide41.In the present study, heat stress was achieved by delaying sowing by more than three months. This could have introduced confounding effects as delayed sowing changes not only temperature but also photoperiod. However, the photoperiod effect in this study is considered minimal for several reasons. Firstly, the lines presented in this study were selected using the shuttle breeding technique that characterises CIMMYT’s wheat breeding strategy and selects lines relatively insensitive to photoperiod and vernalisation response. This is because one selection site has a long photoperiod and negligible vernalizing coldDe novo SNP discovery is the process of generating SNP markers from high-throughput next-generation sequencing as opposed to using lower density genotyping arrays. The value of this approach in breeding efforts is starting to be more widely recognised. In conjunction with high throughput phenotyping methods42, high density, unbiased markers can be leveraged to discover MTAs or to narrow pre-existing QTL intervals to provide more robust markers for global breeding programs44. Using these methods, we have identified alleles at three pleiotropic MTAs on chromosomes 1B, 2B and 6D that when stacked increase yield by 56.5% and reduce canopy temperature by 1.97 °C/2.37 °C under heat stress conditions when compared to lines containing the three major alleles at these positions  uncovered several candidate genes for further dissection. Candidate genes are by their nature speculative but may provide a starting point for follow-up studies aiming to map the causal genes. As the 6D Ae. tauschii segment appears to be actively recombining, it should be possible to precisely dissect this region and map the causal gene. Our proposed candidate genes for the 6D MTA differ from the gene proposed by Singh et al.13. By using the Ae. tauschii genomes rather than relying solely on the wheat reference genome, we have demonstrated that the isoflavone reductase gene Singh et al.13 proposed is not present in the core introgressed region. This difference and the introgressed candidate gene not found in wheat identified highlight the importance of considering non-reference genomes downstream of a GWAS, particularly when divergent material has been introduced, as the variation underlying the trait of interest might be absent from the reference genome.Following the identification of the core introgressed region, we extracted the syntenic region from five 46. The donor lines for these markers will be selected using our introgression mapping approach to introduce minimal linkage drag alongside the traits of interest. Efforts to develop KASP markers for the favourable MTA alleles are currently ongoing at CIMMYT. The germplasm is available to the community through IWYP.org request.These three markers can be deployed into marker-assisted breeding or introgression pipeline programmes to incorporate heat resilience traits into elite cultivars. The fact that no yield penalty was identified under more favourable conditions adds value to their deployment, especially given the negative impact that has been documented in terms of yield stability under increasing temperatures using extensive international data21. These exotic lines include primary synthetic derivative lines, containing between 0.5% and 43% donor material25; Mexican and other origin landraces derivatives; and Elite lines containing an introgressed segment of Th. ponticum on chr7D and/or S. cereale on chr1B25. The set of Elite lines contain 11 CIMMYT varieties released from 1966 until 2007 and additional lines derived from the systematic screening under yield potential and heat stressed field conditions of CIMMYT breeding and pre-breeding material. This allowed the identification of elite genotypes with favourable expression of traits of interest such as high biomass/RUE at different growth stages including final above ground biomass under both yield potential and heat stressed conditions. In general, pre-breeding material is derived from crosses where one of the parents was selected for expressing low canopy temperature and/or high yield or biomass under heat stressed environments.The High Biomass Association Mapping Panel HiBAP I consists of 149 spring wheat lines  and heat stressed conditions (Ht16 and Ht17). Heat stressed conditions were created with delayed sowing where emergence was registered in March instead of November or December as in a normal growing cycle  with four (YP16) and two (YP17) rows per bed (0.1 m and 0.24 m between rows respectively) and 4 m long. For heat stressed experiments, two replicates were evaluated for HiBAP I in 2 m × 0.8 m plots with three rows per bed  and 50 kg P ha−1 at soil preparation, 50 kg N ha−1 with the first irrigation and another 150 kg N ha−1 with the second irrigation. Rainfall, radiation, maximum, minimum and mean temperature by month for all the years of evaluation are presented in Supplementary Fig. The field experiments were carried out at IWYP-Hub  situated at CIMMYT’s Experimental Station in Campo experimental Norman E. Borlaug (CENEB) in the Yaqui Valley, near Ciudad Obregon, Sonora, Mexico  under fully irrigated conditions for both yield potential and heat stressed experiments. The soil type at the experimental station is a coarse sandy clay, mixed montmorillonitic typic caliciorthid. It is low in organic matter and is slightly alkaline (pH 7.7)48, following the average phenology of the plot . The phenological stages recorded were heading for heat experiments , anthesis for yield potential experiments  and physiological maturity  for both experiments. Percentage of grain filling (PGF) was calculated as the number of days between anthesis and physiological maturity divided by DTM.Phenology of the plots was recorded during the cycle using the Zadoks growth scale (GS)-1) and infertile spikelets per spike (InfSPKLSP-1) were also counted in five spikes per plot at PM.Plant height was measured as the length of five individual shoots per plot from the soil surface to the tip of the spike excluding the awns. Spike, awn and peduncle length were measured in five shoots per plot before physiological maturity (PM). Fertile (SPKLSP49. Samples of 100 (YP16), 50 (YP17) or 30  fertile shoots were taken from the harvested area at physiological maturity to estimate yield components. The sample was oven-dried, weighed and threshed to allow calculation of harvest index, biomass at physiological maturity, spikes per square meter, grains per square meter, number of grains per spike and grain weight per spike. Grain yield was determined on a minimum of 3.2 m2 to a maximum of 4.8 m2 under yield potential experiments and 1.6 m2 under heat experiments. In yield potential experiments only, to avoid edge effects arising from border plants receiving more solar radiation, 50 cm of the plot edges were discarded before harvesting. From the harvest of each plot, a subsample of grains was weighed before and after drying (oven-dried to constant weight at 70 °C for 48 h) and the ratio of dry to fresh weight was used to determine dry grain yield and thousand grain weight. Grain number was calculated as (Yield/TGW) × 1000. Biomass at physiological maturity was calculated from yield/HI. Number of spikes per m2 was calculated as biomass at physiological maturity /(shoot dry weight/shoot number).At physiological maturity, grain yield and yield components were determined using standard protocolsAerial measurements data for canopy temperature and NDVI was collected using different aerial platforms. Each year, the logistics and availability determined which platform could be used for measuring the heat trials. A summary of the platforms used, together with the cameras and the achieved resolutions, is presented in Supplementary Table NDVI and canopy temperature orthomosaics were obtained from the aerial images using the software Pix4D. The orthomosaics were then exported to ArcGIS where a grid of polygons representing each polygon was adjusted on top of the image. To avoid the border effect, the polygons were buffered 0.5 m from the north and south border of the plot. Finally, the pixel values were extracted using the ‘raster’ package in R. We extracted the value of all the pixels enclosed within each polygon and removed possible outliers and calculated the average per plot.50.To determine the effect of heat stress in the genotypes evaluated across years and panels, Stress susceptibility index (SSI) was calculated using data from yield potential (Yyp) and heat stressed (Yht) experiments as follows Eq. :1\docume51,). When its effect was significant, DTA/DTH was used as a covariate (fixed effect) except for phenology. Broad sense heritability (H2) was estimated for each trait across both years as follows , σ2 is the error variance, σ2g is the genotypic variance and σ2ge = G×Y variance. Unpaired t tests for stress index (SSI) were conducted with the means across years to determine if the elite and exotic groups presented statistical differences with p-value < 0.001.Data from both panels was analysed by using a mixed model for computing the least square means (LSMEANS) for each line across both years using the program Multi Environment Trial Analysis with R for Windows  and the Quant-iTTM assay kit (Life Technologies). From this DNA, dual indexed Trueseq libraries with an average insert size of 450 bp were produced for each line and enriched using a custom MyBaits 12Mbp  enrichment capture synthesised by Arbour Bioscience and using 8x pre-capture multiplexing. 90,000 of these probes were designed using an island strategy to target regions across the whole genome. A subgenome-collapsed reference was used to design these probe sequences to enable homoeologous regions to be targeted with a single probe, thus expanding the design space. The final 10,000 probes were designed for selected genes, targeting both the gene body and 2Kbp upstream. Post enrichment libraries were sequenced using an S4 flowcell on an Illumina NovaSeq6000 producing 150 bp paired end reads.All genotyping data was taken from ref. 52 using BWA mem v0.7.1353. Samtools v1.454 was used to remove unmapped reads, supplementary alignments, improperly paired reads, and reads that didn’t map uniquely (q < 10). PCR duplicates were removed using Picard’s MarkDuplicates55. SNPs were called using samtools mpileup and bcftools call56 with parameter -m. SNPs were filtered using GATK55 to remove SNPs that were heterozygous, had a quality score <30 or a depth <5. A locus was designated as homozygous reference if no alternative allele was found but 5 or more reads were mapped at that position. To create a set of shared SNPs for use in GWAS, SNPs for all lines were combined and loci with more than 10% missing data and a minor allele frequency (MAF) below 5% were removed. The remaining SNP loci were subjected to imputation using Beagle 5.057.Sequencing reads were trimmed and low-quality reads removed. These reads were mapped to the Chinese Spring RefSeq v1.0 wheat reference genome58 was used to genetically infer the population structure of the panel and produce a population structure matrix. An admixture model was selected and run using 30,000 burn-in iterations and 50,000 Markov Chain Monte Carlo (MCMC) model repetitions for assumed subpopulations of 2–10 for 10 randomly selected, seeded iterations for each assumed subpopulation. The delta k method from59 was applied to all 10 replicates to identify the most likely number of definable subpopulations. This was implemented using the STRUCTURE HARVESTER Python script60. Finally, CLUMPP v1.1.261 was used with 10 independent STRUCTURE replicates to produce a consensus Q matrix for each assumed subpopulation number. GWAS analysis was conducted using the MLM model implemented in GAPITv3.062. Principal component analysis eigenvectors 1–10 or membership coefficient matrices for 3-8 assumed subpopulations deduced above by STRUCTURE were used as covariates in the model to mitigate the effects of hidden familial relatedness. The EMMA method63 was implemented in GAPIT to create a positive semidefinite kinship matrix required by the MLM model. Each MTA flanking interval was deduced by identifying the SNP position furthest upstream and downstream from the highest associated SNP that was above the -log P threshold of 5.STRUCTURE v2.3.452 was split into n genomic windows using bedtools makewindows64. Using the alignments produced in ref. 25 and detailed above, the number of reads mapping to each window was computed using hts-nim-tools65. To normalise by the sequencing depth of each line, read counts were divided by the number of mapped reads that passed the filters, producing normalised read counts c. Different windows of the genome have variable mapping coverage rates, so to compute coverage deviation we must compare each window to the same window in the other lines in the collection. Median normalised read counts, m, were produced, containing the median for each genomic window across the 149 lines. Mapping coverage deviation, d, was then defined for each line as follows  and probability of 0.99. This method was based on ref. 66.RefSeq v1.0lows Eq. :3\documeAe. tauschii reference accession AL8/7826 and 5 additional accessions that represent 5 different clades27, 4 Secale cereale accessions67, Secale. vavilovii67, Thinopyrum ponticum68, and T. aestivum cultivars Weebil68, Norin6168 and Pavon7669 were mapped to RefSeq v1.052, filtered and SNP called as described for the genotyping above and in25. Homozygous SNPs were retained if they had between 10 and 60 reads supporting the alternative allele and an allele frequency > = 0.8. Heterozygous SNPs were retained if they had between 10 and 60 reads mapped and were biallelic with each allele having > = 5 reads in support and an allele frequency > = 0.3. SNPs from one relative species not shared with any of the other species or wheat cultivars were retained as species-specific SNPs. These species-specific SNPs were assigned to HiBAP SNPs if they matched in position and allele. Species-specific SNP ratios were calculated by dividing the number of SNPs in each window matched to a species-specific SNP by the mean number of SNPs matched to that species in that window across HiBAP I. SNP ratio scores below 1.45 were removed to keep enriched scores only.Paired-end whole-genome sequencing data for the Ae. tauschii Aet v4.026 were aligned using Minimap270 with parameters -x asm10. Alignments <2.5 Kb in length or with mapping quality <40 were discarded. The dot plot was produced using pafr R package71. Proteins encoded by genes in the first 10Mbp of 6D in Ae. tauschii and CS were aligned using BLASTp72. Protein alignments and minimap2 alignments were used to anchor either side of the region commonly introgressed in all lines with the 6D T genotype to anchor the region from CS to Ae. tauschii. The Ae. tauschii genes and their proteins within this segment are considered as candidate genes. BLASTp72 was used to compare these proteins to wheat proteins. Protein domains were identified using HMMER hmmscan73 via ebi using Pfam, TIGRFAM, Gene3D, Superfamily, PIRSF, and TreeFam databases.The first 10 Mb of 6D from CS and Ae. tauschii and CS were aligned using BLASTp72. Protein alignments and minimap2 alignments were used to anchor either side of the region commonly introgressed in all lines with the 6D T allele to anchor the region from CS to Ae. tauschii. The sequence extracted from the Ae. tauschii reference genome was aligned to the other 4 chromosome-level assemblies using minimap270 with parameters -x asm5. Alignments below length 5000 or quality of 40 were removed. The coordinates of each orthologous region were determined manually and the genes within these coordinates extracted from the respective gff files. The Ae. tauschii genes and their proteins within these segments were considered as candidate genes for functional exploration. BLASTp72 was used to compare these proteins to wheat proteins. Protein domains were identified using HMMER hmmscan73 via ebi using Pfam, TIGRFAM, Gene3D, Superfamily, PIRSF, and TreeFam databases. Novelty of genes was determined by aligning the extracted protein sequence to each genome using tblastn72.Proteins encoded by genes in the first 10Mbp of 6D in 28. The knowledge networks created for each gene were then studied to identify links to the trait from which each MTA was deduced including their biochemical function and orthologous genes being linked in other organisms such as Rice and Arabidopsis thaliana. For the Ae. tauschii genes introgressed into the 6D interval, we conducted extensive literature searches to identify genes with links to heat stress response based on functional studies of related genes.The genes in each identified interval  were submitted to Knetminer52 using tblastn72. We mapped Chinese Spring RNAseq data from leaf, root and shoot to RefSeq v1.052 using HISAT274 and assembled transcripts using cufflinks75. We visually inspected the coding sequencing and RNA-Seq alignments using IGV76, which showed that the MYB-binding domain exon is present and expressed in wheat. To check whether the protein has a premature stop codon, we extracted the coding sequence from the assembled transcript and checked for the presence of a complete open reading frame with no stop codons using EMBOSS getorf77. Finally, we checked the presence of intact domains with HMMER hmmscan73 via ebi using Pfam, TIGRFAM, Gene3D, Superfamily, PIRSF, and TreeFam databases. To explore qualitative expression of candidate genes, we mapped Ae. tauschii RNAseq data from leaf, root, seedling and developing grain 10dd (PRJEB23317) to Aet v4.026 as above and abundances were counted using StringTie78, taking the mean transcripts per million (TPM) across the replicates. Qualitative expression of the CS orthologues was explored using Wheat Expression Browser79 and the previously leaf, root and shoot RNAseq data mapped above.To test whether the missing myb-binding domain in the TraesCS6D02G014900 annotation was real or an artefact, we manually reannotated the gene. We identified the exon containing the MYB-binding domain in the wheat orthologue by aligning the coding sequence from the tauschii orthologue to Chinese Spring RefSeq v1.0Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary-New