DOI: 10.5061/dryad.9s4mw6mpt
title: Genomic characterisation and dissection of the onset of resistance to acetyl CoA carboxylase-inhibiting herbicides in a large collection of Digitaria insularis from Brazil

description: An in-depth genotypic characterisation of a diverse collection of
 Digitaria insularis was undertaken to explore the neutral genetic
 variation across the natural expansion range of this weed species in
 Brazil. With the exception of Minas Gerais, populations from all other
 states showed high estimates of expected heterozygosity (HE &gt; 0.60)
 and genetic diversity. There was a lack of population structure based on
 geographic origin and a low population differentiation between populations
 across the landscape as evidenced by an average Fst value of 0.02. On
 combining haloxyfop [acetyl CoA carboxylase (ACCase)-inhibiting herbicide]
 efficacy data with neutral genetic variation, we found evidence of the
 presence of two scenarios of resistance evolution in this weed species.
 Whilst populations originating from north-eastern region demonstrated an
 active role of gene flow, populations from the mid-western region
 displayed multiple, independent resistance evolution as the major
 evolutionary mechanism. A target-site mutation (Trp2027Cys) in the ACCase
 gene, observed in less than 1% of resistant populations, could not explain
 the reduced sensitivity of 15% of the populations to haloxyfop. The
 genetic architecture of resistance to ACCase-inhibiting herbicides was
 dissected using a genome-wide association study (GWAS) approach. GWAS
 revealed the association of three SNPs with reduced sensitivity to
 haloxyfop and clethodim. In silico analysis of these SNPs
 revealed important non-target site genes belonging to families involved in
 herbicide detoxification, including UDPGT91C1 and GT2, and genes involved
 in the vacuolar sequestration-based degradation pathway. Exploration of
 five genomic prediction models revealed that the highest prediction power
 (≥ 0.80) was achieved with the models Bayes A and RKHS, incorporating SNPs
 with additive effects and epistatic interactions, respectively.
description: <strong>Plant material</strong> A
 total of 205 <em>D. insularis</em> weed populations were used
 in the study. The populations were collected from the most relevant
 soybean- (and corn) growing regions across Brazil inhabiting crop fields
 and crop margins in the southern, mid-western, south-eastern, and
 north-eastern regions. Briefly, these 205 populations originated from
 seven states including Mato Grosso (77), Mato Grosso do Sul (10), Minas
 Gerais (19), Bahia (24), Goiás (20), Paraná (PR) and Rio Grande do Sul
 (18). The detailed information on the geographic origin and geographic
 coordinates are provided in the supplementary table (Table S1). Some
 collection sites (1/5<sup>th</sup> of the collection) had a
 history of multiple herbicide treatments on the crop including glyphosate,
 ACCase inhibitors such as haloxyfop or clethodim alone, or in mixtures
 and/or ALS inhibitors (Table S1). The populations were collected in 2020
 and 2021 and were investigated for their sensitivity to ACCase inhibitors
 haloxyfop and clethodim at multiple dose rates by the Weed Research team
 at Brazil Resistance Management Laboratory in Uberlândia, Brazil, as part
 of resistance monitoring studies.
 <strong>Herbicide resistance screening of <em>D.
 insularis</em> collection</strong> The
 rates used in the population screening were initially determined in a
 pilot study using ten <em>D.
 insularis-</em>sensitive populations sampled in Brazil's urban
 areas with no history of herbicide application. Informative herbicide
 rates were determined as 7.8; 15.0, 27.0, and 64.0 g a.i ha<sup>-1
 </sup>for haloxyfop and 27.0, 54.0, and 108.0 g a.i
 ha<sup>-1</sup> for clethodim. Seeds from
 all <em>D. insularis </em>populations were germinated and
 planted in 1 L pots filled with a commercial substrate to produce around
 15 plants per pot. Each combination of population and herbicide dose was
 replicated 3 times (45 plants tested per treatment) in a completely
 randomized design. The herbicide treatments were sprayed on plants at the
 4 leaf-stage, in a spray chamber equipped with flat fan nozzles calibrated
 to deliver 200 L ha-1 at 200 kPa pressure. Plant control was evaluated 21
 days after treatment, using a visual scale of 0 to 100%; 0% represents
 healthy plants and the absence of symptoms, and 100% represents the death
 of the plant. <strong>DNA extraction,
 Genotyping-by-sequencing (GBS) library preparation and
 sequencing</strong> Four pinches of seeds were
 sown in a punnet of size 18 cm x 6 cm filled with peat keeping 3cm between
 pinches. For each population, two such punnets were sown. The punnets were
 watered manually and put on trolleys in a glass house with controlled
 conditions (day temperature, 24<sup>0</sup>C; night
 temperature, 18<sup>0</sup>C; light, 16 hrs; humidity -65%).
 The punnets were watered daily for three weeks. After three weeks, a 1cm x
 2cm of leaf sample was cut from 25 individual plants for each population
 and pooled into a 14ml falcon tube. The falcon tubes were stored in a −80
 °C freezer until further manipulation. The leaf samples
 were dried in a freeze dryer for three consecutive days and nights by
 keeping the shelves at a contact temperature of 1.0
 <sup>0</sup>C and the freezer at
 -60 <sup>0</sup>C. After freeze drying, the samples were
 shipped to LGC Genomics GmbH, Germany for DNA extraction, reduced
 representation library preparation, and sequencing.
 Genomic DNA extraction was performed from the pooled samples
 using the sbeadex™ maxi plant kit (LGC) on KingFisher Flex (after the
 lysis step) followed by a spectrophotometric quantification step using
 Nano Drop 8000 (Thermo Fisher Scientific). Reduced representation library
 preparation was done by the standardized ddRAD protocol at LGC Genomics.
 Briefly, 100 ng of genomic DNA were digested with 2 units each
 of <em>Apek</em> I and <em>Pst</em> I enzymes
 (NEB) in 1 times NEB buffer 3.1 in 20μl volume for one hour at 37°C. The
 restriction enzymes were heat-inactivated by incubation at 75°C for 60
 min. The detailed protocol for the ligation reaction, library
 purification, amplification, and normalization were performed according to
 the standardized ddRAD protocol at LGC Genomics, GmbH. The library was
 size selected on a LMP-Agarose gel, removing fragments smaller than 300 bp
 and those larger than 500 bp. Sequencing was done on an Illumina NovaSeq
 6000 (150bp paired-end read). <strong>Genotypes
 and SNP filtering</strong> Demultiplexing of all
 libraries for each sequencing lane was done using the Illumina bcl2fastq
 v2.20 software. Demultiplexing of library groups into samples was done
 according to their inline barcodes and verification of the restriction
 site. No mismatches or Ns were allowed in the inline barcodes, but Ns were
 allowed in the restriction site. Reads with final length &lt; 20 bases
 were rejected and reads with 5’ ends not matching the restriction enzyme
 site were also discarded. The reads were quality trimmed at 3’-end to get
 a minimum average Phred quality score of 20 over a window of ten bases.
 The mapping of quality trimmed reads on the<em> D.
 insularis</em> reference genome v01.0 (available at Weedpedia, https://weedpedia.weedgenomics.org/) was done using BWA-MEM v0.7.12. One combined alignment file of all samples in the BAM format was used for variant discovery and genotyping of samples with Freebayes v1.2.0. Filtering of variants was done using the following GBS-specific rule set; 1.     The read count for a locus must exceed 8 reads 2.     Genotypes must have been observed in at least 66% of samples 3.     Minimum allele frequency across all samples must exceed 5%. <strong>Genetic diversity and population differentiation</strong> The genetic diversity indices expected heterozygosity (H<sub>E</sub>) and inbreeding coefficients (<em>F</em><sub>IS</sub>) were calculated using the R packages ‘adegenet’ and ‘hierfstat’ (Jerome Goudet, 2005). The polymorphic information content (PIC) was calculated using an in-house R package. The two- and three-dimensional principal component analysis (PCA) was conducted using the R packages ‘stats’ and ‘rgl’. A Bayesian clustering approach implemented in the program STRUCTURE version 2.3.4 (Pritchard et al., 2000) was used to analyse population genetic structure by setting the replication number to 10,000 for the burn-in and Markov Chain Monte Carlo (MCMC) iterations each and using options of admixture model and correlated allele frequencies. The number of subpopulations i.e., K was set from 1 to 7 and three independent runs were performed for each K. The Structure Harvester (https://taylor0.biology.ucla.edu/structureHarvester/) was used to analyze the results from the STRUCTURE software, which constructs a deltaK vs K plot using the method of Evanno et al. (2005). The weighted neighbour joining (NJ) was constructed in DARwin 6.0. (Perrier and Jacquemoud-Collet, 2006). <strong>Target site mutations in ACCase </strong> Two primer pairs were used to amplify the ACCase gene sequence in <em>D. insularis</em>: FE35332 Forward (5′-ATGTCCACTCCTGAATTCCCA-3′), FE35333 Reverse (5′-CATTCTGAGGGAAGTATCAT-3′). PCR was performed in 25 μL reaction volume containing 5.0 µL of GoTaq Buffer, 0.5 µL of 10 mM dNTPs, 1.5 µL of 25 mM MgCl<sub>2</sub>, 0.5 µL of 10 µM of each forward and reverse primers, 0.2 µL of GoTaq G2 Hot Start Polymerase (Promega), and 14.8 µL of ultrapure nuclease-free water (Sigma). PCR cycling conditions were: one cycle at 95 °C for 2 min, 35 cycles at 94 °C for 30 s, 58 °C for 30 s, 72 °C for 90 s, and final extension at 72 °C for 10 min. PCR product was run on 1.0% agarose gel to verify the amplicon size of 1.5kb. The amplified samples were purified and sequenced in a Genetic Analyzer 3500 instrument (Applied Biosystems, Thermo Fisher) following the manufacturer’s instructions. Four individuals per population were sequenced using the original amplification primers and the following three internal sequencing forward primers: FE35334 Sequencing Forward 1 (5’- TGGGAGAGCAAAGCTTGGGGT-3’), FE35407 Sequencing Forward 2 (5’- GAAGTGCTGCTATTGCCAGTGC -3’) and FE35408 Sequencing Forward 3 (5’- GACCCACCAGACAGACCTGTTA -3’). The chromatograms were manually read using Bioedit version 7.2.5 software (Hall, 1999) to screen the seven known target-site mutations. <strong>Genome-wide association study (GWAS) and <em>in silico</em> analysis of significant SNPs</strong> The normality of the resistance data scores was checked in PAST3 program (Hammer et al., 2001). The resistance scores data of haloxyfop at a dose rate of 7.8 g a.i ha<sup>-1 </sup>and clethodim at a dose rate of 27.0 g a.i ha<sup>-1 </sup>showed normal and near-normal distribution, respectively, and hence were used in GWAS. Both the general linear model (GLM) and mixed linear model (MLM) were used for GWAS in TASSEL software ver 5.0 (Bradbury et al., 2007). The kinship matrix was calculated using VanRaden algorithm (VanRaden, 2008) in the GAPIT package 2.0 (Lipka et al., 2012). In the GLM, PCA was used as a fixed variate and in the MLM, PCA and kinship matrices were used as fixed and random variates, respectively. The threshold to declare significant marker-trait associations (MTA) was ≥10<sup>−3</sup> (log10p) after applying a correction for a false discovery rate (FDR) at <em>p</em> &lt; 0.05. The VCF file was annotated with SnpEff version 5.1 using the IWGC <em>Digitaria insularis</em> reference genome and annotation v01.0. In addition, the in-silico analysis of the significant SNPs was conducted using nucleotide Basic Local Alignment Search Tool (BLAST) in the EnsemblPlants database (https://plants.ensembl.org/index.html). The EnsemblPlants database has cDNA/transcript sequences of more than 80 monocots and dicots and of model plants, which were used to find the homologies. The genes found in the overlapping region and within 1.0 Mb upstream and downstream of the matched regions were selected as candidate genes. To determine their molecular functions, the protein sequences of the candidate genes were downloaded from EnsemblPlants database and used in protein BLAST analysis in NCBI server (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and their molecular functions were determined after ascertaining their homologies with known proteins in grasses.  <strong>Genomic prediction models</strong> Four parametric models (Ridge regression, Bayes A, Bayes B, Bayes C) and a non-parametric model (RKHS) were used and all these models are implemented in the ‘BGLR’ package (De Los Campos et al., 2022) in R. Ridge regression (RR) method considers common variance for all markers and shrinks the marker effects toward zero (Meuwissen et al., 2001). All Bayesian models do not consider the common variance of markers and incorporate additive genetic effects. The RKHS model uses a kernel function and captures non-additive effects (epistatic interactions). Either all available SNP markers were used or different sets of SNP markers were employed according to preliminary GWAS results. The SNP markers were ranked according to increasing p-values in GWAS analyses. The prediction accuracy of all models was calculated through “Pearson correlation coefficient” between observed and predicted values based on 100 iterations and 10-fold cross-validations.
description: # Genomic characterization and GWAS of Digitaria insularis
 An in-depth genotypic
 characterisation of a diverse collection of Digitaria insularis was
 undertaken to explore the neutral genetic variation across the natural
 expansion range of this weed species in Brazil. With the exception of
 Minas Gerais, populations from all other states showed high estimates of
 expected heterozygosity (HE &gt; 0.60) and genetic diversity. There was a
 lack of population structure based on geographic origin and a low
 population differentiation between populations across the landscape as
 evidenced by average Fst value of 0.02. On combining haloxyfop [acetyl CoA
 carboxylase (ACCase)-inhibiting herbicide] efficacy data with neutral
 genetic variation, we found evidence of presence of two scenarios of
 resistance evolution in this weed species. Whilst populations originating
 from north-eastern region demonstrated an active role of gene flow,
 populations from the mid-western region displayed multiple, independent
 resistance evolution as the major evolutionary mechanism. A target-site
 mutation (Trp2027Cys) in the ACCase gene, observed in less than 1% of
 resistant populations, could not explain the reduced sensitivity of 15% of
 the populations to haloxyfop. Tthe genetic architecture of resistance to
 acetyl CoA carboxylase (ACCase)-inhibiting herbicides was dissected using
 a genome wide association study (GWAS) approach. GWAS revealed association
 of three SNPs with reduced sensitivity to haloxyfop and clethodim. In
 silico analysis of these SNPs revealed important non-target site genes
 belonging to families involved in herbicide detoxification, including
 UDPGT91C1 and GT2, and genes involved in vacuolar sequestration-based
 degradation pathway. Exploration of five genomic prediction models
 revealed that the highest prediction power (≥ 0.80) was achieved with the
 models Bayes A and RKHS, incorporating SNPs with additive effects and
 epistatic interactions, respectively. **Plant material** A total of 205 D.
 insularis weed populations were used in the study. The populations were
 collected from the most relevant soybean- (and corn) growing regions
 across Brazil inhabiting crop fields and crop margins in the southern,
 mid-western, south-eastern and north-eastern regions. Briefly, these 205
 populations originated from seven states including Mato Grosso (77), Mato
 Grosso do Sul (10), Minas Gerais (19), Bahia (24), Goiás (20), Paraná (PR)
 and Rio Grande do Sul (18). The detail information on the geographic
 origin and geographic coordinates are provided in the supplementary table
 (Table S1). Some collection sites (1/5th of the collection) had a history
 of multiple herbicide treatments on the crop including glyphosate, ACCase
 inhibitors such as haloxyfop or clethodim alone or in mixtures and/or ALS
 inhibitors (Table S1). The populations were collected in 2020 and 2021 and
 were investigated for their sensitivity to ACCase inhibitors haloxyfop and
 clethodim at multiple dose rates by Weed Research team at Brazil
 Resistance Management Laboratory in Uberlândia, Brazil, as part of
 resistance monitoring studies. **Herbicide resistance screening of D.
 insularis collection** The rates used in the population screening were
 initially determined in a pilot study using ten D. insularis sensitive
 populations sampled in Brazil urban areas with no history of herbicide
 application. Informative herbicide rates were determined as 7.8; 15.0,
 27.0 and 64.0 g a.i ha-1 for haloxyfop and 27.0, 54.0 and 108.0 g a.i ha-1
 for clethodim. Seeds from all D. insularis populations were germinated and
 planted in 1 L pots filled with a commercial substrate to produce around
 15 plants per pot. Each combination of population and herbicide dose was
 replicated 3 times (45 plants tested per treatment) in a completely
 randomized design. The herbicide treatments were sprayed on plants at the
 4 leaf-stage, in a spray chamber equipped with flat fan nozzles calibrated
 to deliver 200 L ha-1 at 200 kPa pressure. Plant control was evaluated 21
 days after treatment, using a visual scale of 0 to 100%; 0% represents
 healthy plants and the absence of symptoms, and 100% represents the death
 of the plant. **DNA extraction, Genotyping-by-sequencing (GBS) library
 preparation and sequencing** Four pinches of seeds were sown in a punnet
 of size 18 cm x 6 cm filled with peat keeping 3cm between pinches. For
 each population, two such punnets were sown. The punnets were watered
 manually and put on trolleys in a glass house with controlled conditions
 (day temperature, 240C; night temperature, 180C; light, 16 hrs; humidity
 -65%). The punnets were watered daily for three weeks. After three weeks,
 a 1cm x 2cm of leaf sample was cut from 25 individual plants for each
 population and pooled into a 14ml falcon tube. The falcon tubes were
 stored in a −80 °C freezer until further manipulation. The leaf samples
 were dried in a freeze dryer for three consecutive days and nights by
 keeping the shelves at a contact temperature of 1.0 0C and freezer at -60
 0C. After freeze drying, the samples were shipped to LGC Genomics GmbH,
 Germany for DNA extraction, reduced representation library preparation and
 sequencing. Genomic DNA extraction was performed from the pooled samples
 using the sbeadex™ maxi plant kit (LGC) on KingFisher Flex (after lysis
 step) followed by a spectrophotometric quantification step using Nano Drop
 8000 (Thermo Fisher Scientific). Reduced representation library
 preparation was done by the standardized ddRAD protocol at LGC Genomics.
 Briefly, 100 ng of genomic DNA were digested with 2 units each of Apek I
 and Pst I enzymes (NEB) in 1 times NEB buffer 3.1 in 20μl volume for one
 hour at 37°C. The restriction enzymes were heat inactivated by incubation
 at 75°C for 60 min. The detailed protocol for the ligation reaction,
 library purification, amplification and normalization were performed
 according to the standardized ddRAD protocol at LGC Genomics, GmbH. The
 library was size selected on a LMP-Agarose gel, removing fragments smaller
 than 300 bp and those larger than 500 bp. Sequencing was done on an
 Illumina NovaSeq 6000 (150bp paired-end read). **Genotypes and SNP
 filtering** Demultiplexing of all libraries for each sequencing lane was
 done using the Illumina bcl2fastq v2.20 software. Demultiplexing of
 library groups into samples was done according to their inline barcodes
 and verification of restriction site. No mismatches or Ns were allowed in
 the inline barcodes, but Ns were allowed in the restriction site. Reads
 with final length &lt; 20 bases were rejected and reads with 5’ ends not
 matching the restriction enzyme site were also discarded. The reads were
 quality trimmed at 3’-end to get a minimum average Phred quality score of
 20 over a window of ten bases. The mapping of quality trimmed reads on the
 D. insularis reference genome v01.0 (available at Weedpedia,
 ) was done using BWA-MEM
 v0.7.12. One combined alignment file of all samples in the BAM format was
 used for variant discovery and genotyping of samples with Freebayes
 v1.2.0. Filtering of variants was done using the following GBS-specific
 rule set; 1. The read count for a locus must exceed 8 reads 2. Genotypes
 must have been observed in at least 66% of samples 3. Minimum allele
 frequency across all samples must exceed 5%. **Genetic diversity and
 population differentiation** The genetic diversity indices expected
 heterozygosity (HE) and inbreeding coefficients (FIS) were calculated
 using the R packages ‘adegenet’ and ‘hierfstat’ (Jerome Goudet, 2005). The
 polymorphic information content (PIC) was calculated using an in-house R
 package. The two- and three-dimensional principal component analysis (PCA)
 was conducted using the R packages ‘stats’ and ‘rgl’. A Bayesian
 clustering approach implemented in the program STRUCTURE version 2.3.4
 (Pritchard et al., 2000) was used to analyse population genetic structure
 by setting replication number to 10,000 for the burn-in and Markov Chain
 Monte Carlo (MCMC) iterations each and using options of admixture model
 and correlated allele frequencies. The number of subpopulations i.e., K
 was set from 1 to 7 and three independent runs were performed for each K.
 The Structure Harvester
 () was used to
 analyze the results from the STRUCTURE software, which constructs a deltaK
 vs K plot using the method of Evanno et al. (2005). The weighted neighbour
 joining (NJ) was constructed in DARwin 6.0. (Perrier and
 Jacquemoud-Collet, 2006). **Target site mutations in ACCase** Two primer
 pairs were used to amplify the ACCase gene sequence in D. insularis:
 FE35332 Forward (5′-ATGTCCACTCCTGAATTCCCA-3′), FE35333 Reverse
 (5′-CATTCTGAGGGAAGTATCAT-3′). PCR was performed in 25 μL reaction volume
 containing 5.0 µL of GoTaq Buffer, 0.5 µL of 10 mM dNTPs, 1.5 µL of 25 mM
 MgCl2, 0.5 µL of 10 µM of each forward and reverse primers, 0.2 µL of
 GoTaq G2 Hot Start Polymerase (Promega), and 14.8 µL of ultrapure
 nuclease-free water (Sigma). PCR cycling conditions were: one cycle at 95
 °C for 2 min, 35 cycles at 94 °C for 30 s, 58 °C for 30 s, 72 °C for 90 s,
 and final extension at 72 °C for 10 min. PCR product was run on 1.0%
 agarose gel to verify the amplicon size of 1.5kb. The amplified samples
 were purified and sequenced in a Genetic Analyzer 3500 instrument (Applied
 Biosystems, Thermo Fisher) following manufacturer’s instructions. Four
 individuals per population were sequenced using the original amplification
 primers and the following three internal sequencing forward primers:
 FE35334 Sequencing Forward 1 (5’- TGGGAGAGCAAAGCTTGGGGT-3’), FE35407
 Sequencing Forward 2 (5’- GAAGTGCTGCTATTGCCAGTGC -3’) and FE35408
 Sequencing Forward 3 (5’- GACCCACCAGACAGACCTGTTA -3’). The chromatograms
 were manually read using Bioedit version 7.2.5 software (Hall, 1999) to
 screen the seven known target-site mutations. **Genome wide association
 study (GWAS) and in silico analysis of significant SNPs** The normality of
 the resistance data scores was checked in PAST3 program (Hammer et al.,
 2001). The resistance scores data of haloxyfop at dose rate of 7.8 g a.i
 ha-1 and clethodim at dose rate of 27.0 g a.i ha-1 showed normal and
 near-normal distribution, respectively and hence were used in GWAS. Both
 general linear model (GLM) and mixed linear model (MLM) were used for GWAS
 in TASSEL software ver 5.0 (Bradbury et al., 2007). The kinship matrix was
 calculated using VanRaden algorithm (VanRaden, 2008) in the GAPIT package
 2.0 (Lipka et al., 2012). In the GLM, PCA was used as a fixed variate and
 in the MLM, PCA and kinship matrices were used as fixed and random
 variates, respectively. The threshold to declare significant marker-trait
 associations (MTA) was ≥10−3 (log10p) after applying a correction for a
 false discovery rate (FDR) at p &lt; 0.05. The VCF file was annotated with
 SnpEff version 5.1 using the IWGC Digitaria insularis reference genome and
 annotation v01.0. In addition, the in-silico analysis of the significant
 SNPs was conducted using nucleotide Basic Local Alignment Search Tool
 (BLAST) in the EnsemblPlants database
 (). The EnsemblPlants
 database has cDNA/transcript sequences of more than 80 monocots and dicots
 and of model plants, which were used to find the homologies. The genes
 found in the overlapping region and within 1.0 Mb upstream and downstream
 of the matched regions were selected as candidate genes. To determine
 their molecular functions, the protein sequences of the candidate genes
 were downloaded from EnsemblPlants database and used in protein BLAST
 analysis in NCBI server ()
 and their molecular functions were determined after ascertaining their
 homologies with known proteins in grasses. **Genomic prediction models**
 Four parametric models (Ridge regression, Bayes A, Bayes B, Bayes C) and a
 non-parametric model (RKHS) were used and all these models are implemented
 in the ‘BGLR’ package (De Los Campos et al., 2022) in R. Ridge regression
 (RR) method considers common variance for all markers and shrinks the
 marker effects toward zero (Meuwissen et al., 2001). All Bayesian models
 do not consider the common variance of markers and incorporate additive
 genetic effects. The RKHS model uses a kernel function and captures
 non-additive effects (epistatic interactions). Either all available SNP
 markers were used or different sets of SNP markers were employed according
 to preliminary GWAS results. The SNP markers were ranked according to
 increasing p-values in GWAS analyses. Prediction accuracy of all models
 was calculated through “Pearson correlation coefficient” between observed
 and predicted values based on 100 iterations and 10-fold
 cross-validations. **Results** **GBS marker distribution** A total of
 105,699 single nucleotide polymorphisms (SNPs) were generated across 205
 populations using the criterion of a minimum coverage to call a SNP. Of
 these, 5,238 SNPs were retained for genetic analysis after filtering for
 5% minor allele frequency (MAF) and missing data 30% and removing markers
 that could not be mapped on the currently available reference genome of D.
 insularis on Weedpedia (). The
 polymorphic information content (PIC) of the filtered SNPs varied from
 0.10 to 0.38. Overall, SNPs showed good distribution with chromosomes S02
 and S06 showing the highest (842) and the least (344) number of SNPs,
 respectively (Fig. 1). On an average, 624Mb of the physical genome was
 encompassed by these SNPs with the maximum genome coverage achieved on
 chromosome S01 (93.7Mb) and minimum (51.9Mb) on chromosome S09 (Table S2).
 **Within populations genetic diversity and population differentiation**
 The expected heterozygosity (HE) and polymorphic information content (PIC)
 values of the populations ranged from 0.56 to 0.64 and 0.20 to 0.37, with
 an average of 0.62 and 0.37, respectively (Table 1). Both parameters, HE
 and PIC, showed that Minas Gerais (MG) population was moderately diverse
 while most other populations showed high level of genetic diversity (HE
 &gt; 0.60). The inbreeding coefficient (FIS) for all the seven populations
 was negative (Table 1) suggesting highly outcrossing nature of the
 populations under local environmental conditions. The average Fst across
 all samples s was 0.020, indicating a low genetic differentiation among
 populations. The pairwise Fst values ranged from 0.003 (between
 populations MS and PR, GO and PR, MT and RS and PR and RS) to 0.064
 (between MG and BA) (Table 2). As expected, a high gene flow (Nm) was
 detected among populations, varying from 3.65 to 83.08, with an overall
 average of 12.25. Interestingly, the lowest Nm estimates were obtained
 between MG and five populations (MT, MS, PR, RS and BA) (Table 2).
 **Principal component analysis (PCA) and STRUCTURE analysis** The PCA plot
 with genome wide 5,238 SNPs revealed two important features; i) all
 populations were scattered in the PCA plot and ii) populations did not
 show clusters based on their geographical origin (Fig. 2). The population
 structure explored by Bayesian STRUCTURE analysis revealed two
 subpopulations at the best K (K = 2) (Fig. S1a, b). Both subpopulations
 comprised of individuals from all seven populations (Fig. S1b). Only
 populations from MG had a defined cluster (cluster II) based on a cluster
 membership threshold of 0.80 (Fig. S1b), whereas all remaining populations
 were distributed between the two clusters. The weighted neighbour joining
 (NJ) tree confirmed that two groups comprised individuals from seven
 populations clustered randomly irrespective of their geographic origin
 (Fig. S2). With the Fst outlier analysis, we detected 63 SNPs that showed
 Fst values higher than the average Fst (Fig. 3a). We used these 63 SNPs to
 redraw PCA to investigate if any geographical differentiation could be
 observed (Fig. 3b). Interestingly, using a subset of 63 SNPs (Fst &gt;
 0.03), MT population could be moderately differentiated from rest of the
 populations while the remaining six populations remained mixed (Fig. 3b).
 To understand the functional significance of this subtle geographical
 differentiation, we conducted an in-silico analysis of the sequences of
 the top fifteen outlier hits (Fst &gt; 0.04) to identify candidate genes
 (Table S3). It was revealed that orthologs of the genes involved in growth
 and development and/or stress pathways showed hits with these sequences.
 **Resistance to ACCase inhibitors and population structure of resistant
 and sensitive populations** As part of the resistance monitoring program,
 the D. insularis populations were tested with, haloxyfop at 7.8, 15.0,
 27.0 and 64.0 g a.i ha-1 ai/ha and clethodim at 27, 54 and 108 g a.i ha-1
 ai/ha. A total of 29 (15.0%), 29 (15.0%), 7 (3.6%) and 6 (3.1%)
 populations were found to show less than 90% control with haloxyfop at
 7.8, 15.0, 27.0 and 64.0 g a.i ha-1, respectively. For clethodim, 16
 (8.2%), 8 (4.1%) and 4 (2.0%) populations were found to be controlled at
 less than 90% at doses 27.0, 54.0 and 108.0 g a.i ha-1, respectively.
 There were no significant differences in the sensitivity scores for the
 two lower doses of haloxyfop i.e., 7.8 and 15.0 g a.i ha-1, hence
 sensitivity scores from the dose 7.8 g a.i ha-1 were used in all further
 analysis. The state wise distribution showed that more than 50% of
 populations with reduced sensitivity to the haloxyfop dose rate 7.8 g a.i
 ha-1 were from MG state followed by MT (31%) (Fig S3a). For higher doses
 of haloxyfop and for all the three doses of clethodim, populations with
 reduced sensitivity were from PR and MS states (Fig. S3b). The efficacy
 scores at 7.8 g a.i ha-1 dose of haloxyfop and 27.0 g a.i ha-1 of
 clethodim showed a normal and near-normal distribution, respectively (Fig.
 S3 c,d). Sequencing of the ACCase gene in all samples revealed that only
 two populations (BR-20-Din-144 and BR-21-Din-046) showed the presence of
 Trp2027Cys mutation. Whilst both populations showed reduced sensitivity to
 all the four doses of haloxyfop (7.8, 15.0, 27.0 and 64.0 g a.i ha-1),
 only one exhibited reduced sensitivity to the two doses (27.0 and 54.0 g
 a.i ha-1) of clethodim. The four populations (BR-21-Din-417,
 BR-21-Din-419, BR-21-Din-421 and BR-21-Din-423), that showed reduced
 sensitivity to the higher doses of haloxyfop (27.0 and 64.0 g a.i ha-1)
 and to all the three doses of clethodim (27.0, 54.0 and 108.0 g a.i ha-1),
 did not show the Trp2027Cys mutation. None of the other known mutations in
 this gene were identified in the resistant populations. To understand
 whether the high gene flow observed in the species is shaping the genetic
 structure of resistance, we investigated the population genetic structure
 of a subset of 65 populations, contrasting for resistance or reduced
 sensitivity to 7.8 g a.i ha-1 of haloxyfop regardless of geographic
 origin. Only 7.8 g a.i ha-1 dose rate was selected for these analyses as
 this dose rate showed a good size of populations with reduced sensitivity
 (29) which could be compared with sensitive populations (36). The other
 dose rates produced too few populations showing reduced sensitivity
 (&lt;8) and hence were not analysed further. The PCA plot showed that
 resistant and sensitive populations were interspersed, and no clear groups
 were observed (Fig. 4a), which led us to suggest that the resistance has
 evolved independently across the landscape. We then drew weighted NJ trees
 of populations from MG and MT regions separately (Fig. 4b, c). For MG, the
 tree showed distinction between resistant and sensitive populations but
 for MT, resistant and sensitive populations were interspersed. **Genome
 wide association study (GWAS) and genomic prediction models** Since
 efficacy scores at 7.8 g a.i ha-1 dose of haloxyfop and 27.0 g a.i ha-1 of
 clethodim showed a normal and near-normal distribution, respectively (Fig
 S3 c,d), characteristics of a quantitative trait, a pilot GWAS study was
 conducted to identify candidate genes associated with reduced efficacy to
 haloxyfop and clethodim. Three and two SNPs were found associated with
 reduced efficacy with haloxyfop in GLM and MLM models, respectively (Fig.
 5). Two SNPs, S02_45439268 and S03_5213977, were common in both models
 (Table 3). S02_45439268 on chromosome 2 explained the highest percentage
 variation (R2) of 26.6% (p value = 3.13 E-06), while S03_5213977 on
 chromosome 3 explained 14.6% R2 (p value = 9.71 E-04). In silico analysis
 (BLAST analysis using reference genomes/cDNA sequences of D. exilis or
 model species) of these SNPs revealed that the SNP S02_45439268 was
 located 0.1Mb upstream of a D. exilis gene Dexi2A01G0009650. The protein
 BLAST analysis of the protein sequence of this gene showed homologies with
 UDP-glycosyltransferase 91C1-like protein (UDPGT91C1) of Panicum virgatum,
 Sorghum bicolor, Oryza glaberrima and Setaria italica. The SNP S03_5213977
 showed homologies with the galactinol--sucrose galactosyltransferase (GT)
 2 in Hordeum vulgare, Aegilops tauschii, Triticum urartu and Lolium
 arundinaceum. For efficacy shift to clethodim, four and one SNP were
 identified with GLM and MLM, respectively (Fig. 6). The four SNPs with the
 GLM model explained 11.3 to 16.6% of R2, while the SNP identified with the
 MLM explained 24.0% of R2 (Table 3). Interestingly, two SNPs were located
 within 0.1Mb of potential NTSR genes. The SNP S05_47637569 was located
 within 0.1Mb of D. exilis gene Dexi1A01G0019350. The BLASTP analysis of
 protein sequence of this gene showed homologies with vacuolar protein
 sorting (VPS)-associated protein 20 of Panicum virgatum, Setaria italica,
 Zea mays and Lolium rigidum. The SNP S08_15270772 is located within 0.1Mb
 of D. exilis gene Dexi9A01G0037640, the protein sequence of which showed
 homologies with CMP-sialic acid transporter 2 of Setaria italica, Panicum
 virgatum and Sorghum bicolor. We further investigated the potential of
 genomic prediction (GP) models to predict NTSR resistance in the panel
 using resistance data scores at haloxyfop dose of 7.8 g a.i ha-1. We
 tested five models, Ridge Regression, Bayes A, Bayes B, Bayes C and RKHS,
 on different subsets of SNPs selected either based on GWAS hits or
 complete set of 5,238 SNPs (Fig. 7). Although predictions based on models
 having 50 SNPs from GWAS showed more than 50% prediction accuracy, the
 highest accuracy was achieved with complete set of 5,238 SNPs with better
 prediction accuracy from Bayes A or RKHS models. ## Description of the
 data and file structure Title of Dataset: Genomic characterisation and
 dissection of the onset of resistance to acetyl CoA carboxylase-inhibiting
 herbicides in a large collection of Digitaria insularis from Brazil Date
 of data collection: 2022-11-25 to 2023-03-15 Geographic location of data
 collection: Jealott’s Hill International Research Centre, Bracknell,
 United Kingdom Information about funding sources that supported the
 collection of the data: No external funding received for this project
 **DATA & FILE OVERVIEW** File List: Phenotype_Data, have the
 phenotypic values of 193 genotypes for reduced sensitivity to haloxyfop
 (7.8 g a.i./ha) and clethodim (27.0 g a.i./ha) Are there multiple versions
 of the dataset? If yes, name of file(s) that was updated: Nil
 **METHODOLOGICAL INFORMATION** Description of methods used for generation
 of data: Seeds from all D. insularis populations were sown in 1 L pots
 filled with a commercial substrate and after germination 15 plants per pot
 were kept. Each combination of population and herbicide dose was
 replicated 3 times (45 plants tested per treatment) in a completely
 randomized design. Plants were sprayed at the 4 leaf-stage and were
 evaluated 21 days after treatment, using a visual scale of 0%–100%; 0%
 represents healthy plants and the absence of symptoms, and 100% represents
 the death of the plant. For genotyping, 25 leaves from 25 individuals of a
 population were pooled and this was done for all populations investigated.
 The populations were genotyped by genotyping-by-sequencing (LGC Genomics
 Ltd., Germany) generating 105,699 genome-wide SNPs. Of these, 5,238 SNPs
 were retained for genetic analysis after filtering for 5% minor allele
 frequency (MAF) and missing data 30% and removing markers that could not
 be mapped on the currently available reference genome of D. insularis on
 Weedpedia (). The normality of
 the resistance data scores was checked in PAST3 program. Principal
 component analysis and population structure analysis were done using R
 package 'rgl' and STRUCTURE program, respectively. Trait
 Analysis by aSSociation Evolution and Linkage (TASSEL) version 5.0 was
 used for GWAS. The kinship matrix was calculated using VanRaden algorithm
 in the GAPIT(Genome Association and Prediction Integrated Tool)package 3.0
 in the R environment. Describe any quality-assurance procedures performed
 on the data: Genotyping data was checked for missing data and
 heterozygosity and only the best high quality SNPs were used for
 population genetics and GWAS. Populations producing more than 40% missing
 data were removed from the final analysis. People involved with sample
 collection, processing, analysis and/or submission: Deepmala Sehgal,
 Claudia Oliveira, Sandra Mathioni, Stephanie Widdison. **DATA-SPECIFIC
 INFORMATION FOR: [Phenotypic data]** Number of variables: 2 Number of
 cases/rows: 193 Variable List: reduced sensitivity to haloxyfop (7.8 g
 a.i./ha) and clethodim (27.0 g a.i./ha) Missing data codes: NA Specialized
 formats or other abbreviations used: Haloxyfop-7.8g, Clethodim-27.0 g
 **DATA-SPECIFIC INFORMATION FOR: [Geno-5.2K]** Number of variables: 5238
 Number of cases/rows: 193 Variable List: 5238 SNPs Missing data codes: N
 Specialized formats or other abbreviations used: A - Adinine, G - Guanine,
 C - Cytosine, T - Thymine, N - Missing, R = A or G, K = G or T, S = G or
 C, Y = C or T, M = A or C, W = A or T ## **SHARING/ACCESS INFORMATION**
 Licenses/restrictions placed on the data: Nil Links to other publicly
 accessible locations of the data: NA Links/relationships to ancillary data
 sets: Nil Was data derived from another source? If yes, list source(s):
 Nil Links to publications that cite or use the data:
 
 Recommended citation for this dataset: Sehgal et al.(2024), Data from:
 Genomic characterisation and dissection of the onset of resistance to
 acetyl CoA carboxylase-inhibiting herbicides in a large collection of
 Digitaria insularis from Brazil ## Code/Software The normality of the
 resistance data scores was checked in PAST3 program. Principal component
 analysis and population structure analysis were done using R package
 'rgl' and STRUCTURE program, respectively. Trait Analysis by
 aSSociation Evolution and Linkage (TASSEL) version 5.0 was used for GWAS.
 The kinship matrix was calculated using VanRaden algorithm in the
 GAPIT(Genome Association and Prediction Integrated Tool)package 3.0 in the
 R environment.