Syzygium cumini L.  is a native, evergreen multipurpose tree species of India. Besides being a fruit tree and for agroforestry in different regions, it is medicinally important too. This study aimed to determine genetic diversity using molecular and phytochemical markers in sixteen genotypes of Indian S. cumini from different agro-ecological zones. The present study used a combination of ISSR markers and the HPLC technique to explore these genotypes. The results showed a wide genetic diversity range based on the similarity coefficient values observed in S. cumini sixteen accessions from different sites. Four primary phenolic acids were discovered in all the accessions; caffeic acid (CA) was found in high concentrations. The intraspecific association between molecular and phytochemical characteristics was the primary goal of this investigation. By employing gene-specific markers for the route of secondary metabolites (polyphenols) production, it further investigated the progressive research of diversity analysis of polyphenol content in S. cumini accessions, which may also expand its nutraceutical and pharmaceutical utilization. Biodiversity loss is now one of the world’s most pressing crises, and parts of the world endowed with rich genetic resources are facing grim scenarios with the alarming rate of loss of biodiversity. IUCN recognizes inadequate coverage of evaluations across all plant categories to report an accurate percentage of vulnerable species for plants. In total, 54% of plant species are currently recognized on the IUCN Red List as threatened . FurtherSyzygium cumini L. , a native, evergreen tree species of India, thrives simply in a tropical climate and is found not only in numerous parts of the Indian sub-continent but also several other countries of Asia and Eastern Africa [S. cumini is medicinally important too, and over many decades, traditional practitioners have employed various methods to manage diabetes [n Africa . In Indin Africa . Besidesdiabetes . DespiteVarious methods can be used to measure a species’ genetic diversity. During the past several decades, comparative anatomy, morphology, embryology, and physiology, as well as other traditional methods of assessing genetic diversity, have been increasingly supported by molecular approaches . Quick sThe exact genotype is guaranteed by DNA fingerprinting, but the active ingredient or chemical components are not disclosed. Consequently, as a method for authenticating germplasm, phytochemical variations are combined with genetic analyses  and biodS. cumini leaves germplasm using ISSR is corroborated. Such research would demonstrate the use of DNA fingerprinting in recognizing the secondary metabolism-related genes in economically significant plants for their breeding programs and, ultimately, for intellectual property rights. This work is the continuation of our previous study in which these germplasms were studied using RAPD markers [This study collected genetic information from different accessions of  markers .Various chemicals and standard DNA molecular weight markers used in fingerprinting studies were of molecular biology grade and obtained from Bangalore Genei Pvt. Ltd. (India). The ISSR primers were custom manufactured from Bangalore Genei Pvt. Ltd. . Standards for TLC and HPLC  were purchased from Fluka Chemica . The solvents used are of HPLC grade and purchased from Rankem .S. cumini from one individual wild tree were collected from four primary agro-ecological zones—moist sub-humid, dry sub-humid semi-arid, and moist sub-humid, arid—and nine sub-zones—leaf samples stood gathered from sixteen specific geographic sites, indicating five states of India: Uttarakhand, Madhya Pradesh, Uttar Pradesh, Karnataka, and Rajasthan  for the ISSR analysis. One negative control (master mix with water in place of DNA template) was added to check for contamination in the experiment. All the molecular analyses were conducted in three replicates.The amount of DNA isolated was measured using agarose gel electrophoresis. Twenty-six ISSR markers  were empA submerged gel electrophoresis midi unit from Bangalore Genei Pvt. Ltd. was used for fractionating the PCR products on an agarose gel. A low-range DNA ruler was included on one side of the gel as a molecular standard. The gel was visualized on a UV transilluminator , photographed, and analyzed using the Kodak gel documentation system (Model EDAS 290) using Kodak ID Image analysis software.S. cumini germplasm with chromatographic techniques TLC and HPLC.This study generated a chem profile of different Extraction and estimation of polyphenolsThe Folin-Ciocalteu technique was expended to ascertain the total polyphenol contented (TPC) . Samples2.Separation of polyphenols using TLC3, and 1% methanolic KOH.For the TLC analysis, standards  and polyphenol samples were exposed to qualitative TLC analysis on cellulose plates . Extracted polyphenol samples (20 µL) were spotted on the plate and developed under the following mobile phase conditions: (1) benzene: acetic acid: water (37:45:18) (2) ethyl acetate: acetic acid: water (60:20:10) (3) ethyl acetate: methanol: water (10:1.65:1.35) (4) ethyl acetate: methanol: formic acid: water (100:13.5:2.5:10) (5) ethyl acetate: toluene: acetic acid (50:40:20) (6) chloroform: acetic acid (90:10) (7) ethyl acetate: formic acid: acetic acid: water (100:11:11:27) (8) ethyl acetate: acetic acid (80:20) (9) ethyl acetate: methanol (80:20). The established plates remained to soak up at room temperature, protected from light and were assessed under UV light at 254 nm. Further, the developed spots were visualized with general sprays for finding phenols, 2% aqueous FeCl3.Separation of polyphenols using HPLC2 and a minimum of 0 Kgf/cm2 were sustained, with a solvent ratio of 30:70 using A and B solvents, respectively, at a wavelength of 320 nm and 1 mL of flow rate with a binary mode of gradient system. Peak areas and retention times were computed using a Pentium computer and a Shimadzu Class-VP integrator.Standards  stock solutions of 1 mg/mL concentration were arranged. The samples were examined using an HPLC system  with solvents A  and B , along with an analog SCL 10 AVP pump, an injection (20 µL loop), and an SPD 10 AVP detector. Data acquisition was made using Class VP Software. Chromatography was performed on a C8 column . A maximum pressure of 400 kgf/cm4.Statistical AnalysisS. cumini samples the correlation analysis was performed using XLSTAT-2015 software [The ISSR data cast off to accumulate a separate binary matrix for cluster examination using the NTSYS-pc 2.1 . The SIMsoftware .S. cumini was challenging because of high levels of polyphenols, tannins, and polysaccharides, which on cell disruption, form a sticky gelation in which nucleic acids become embedded. However, in the present case, overall, a good yield of genomic DNA was obtained from most of the samples of S. cumini collected from different locations. Genetic DNA, yield, and purity variations could be explained as each plant is grown in different climatic conditions, which might result in varying types and amounts of secondary metabolites, reflecting the DNA isolation and yield.Though the plant species are the same, the molecular weight of genomic DNA ranged from 19.5 kb to 20.4 kb . KulkarnS. cumini populations, of which 12 primers gave amplification. However, only 8 primers generated a clear, reproducible pattern  in the resemblance coefficient values, signifying a broad genetic base of 16 genotypes investigated in the present study. Accessions from Saharanpur (CD-5.4) and Meerut (D-4.3) that belonged to dry sub-humid and semi-arid zone, respectively, were found most similar to each other (similarity coefficient 0.89). Similarly, samples collected from neighboring cities of Meerut (D-4.3) and Kanpur (CD-5.4), which too belonged to dry sub-humid and semi-arid zone, respectively, had a high similarity coefficient (0.87). The last value of similarity coefficient (0.45) was detected between the genotypes from Nazibabad (CD-6.1) and Kota (D-4.2), which fitted to moist sub-humid and semi-arid zone, respectively. Similarly, Kota (D-4.2) and Bangalore (D-4.4) belonged to the same zone, i.e., semi-arid had a low similarity coefficient value (0.47)  . Among sThe UPGMA method was used to build a dendrogram based on the resemblance matrix expressing Jaccard’s coefficient to assess the relationship between the genotypes. The dendrogram  shows thDespite the substantial diversity among the samples, UPGMA analysis did not reveal grouping built on areas or agro-climatic sub-zones. Apart from cluster analysis, the principal component analysis (PCA) was performed on group accessions . ClusterS. cumini populations. The Hο within the semi-arid region ranged from 1.17 to 4.09, with a mean value of 2.65. Similarly, the lower and higher values of Hο for dry sub-humid and moist sub-humid varied from 0.69 to 2.16 and 1.18 to 2.56, respectively. The means of Hο for these two regions are 1.82 and 2.04, correspondingly. The Hο might not be analyzed for the arid zone, as only one sample was from this district. The average diversity at a population level (Hcol) varied from a lower value of 1.03 to a higher value of 1.86 with a mean value of 1.54, whereas the observed genetic diversity at the species level (Hsp) ranged from 2.48 to 3.98 with a mean value of 3.00. A comparable value of diversity at the species level  is reported in Mulberry [Based upon ISSR variation, Mulberry .The coefficient of genetic differentiation (Gst) varied from a lower value of 0.38 to a higher value of 0.58 with an average of 0.46, which specified a high degree of genetic differentiation among populations. It presented that 46% of genetic variation exists among populations and 54% within the inhabitants. For woody plants, wind and cross-pollination accounted for 10% of genetic diversity among communities . The estChangium smyrmiodes [Eurya nitida [Phaseolus [Mikania [Nm, or the degree of gene flow, was only calculated to be 0.30. The result is comparable with earlier studies, as the reported value of gene flow was 0.44 in yrmiodes  and 0.24a nitida . In conthaseolus  and 0.91[Mikania . The litS. cumini populations, indicating sufficient gene flow across populations to offset the effects of random drift. Genetic drift affects the genetic structure and heightens population divergence when populations are small and separate [The estimates of Nm were 1.0 for separate . Consequseparate ,32,33.−1. The Folin-Ciocalteu colorimetric assay in this study detects all hydroxylated phenolic compounds. The total phenolics in diverse samples of S. cumini are represented in −1), followed by Jodhpur (13.16 OD units.gdw−1) sample. It is understandable because these samples belong to dry sub-humid and arid zones, with water stress conditions and stress plants producing higher polyphenol content [Each plant’s leaf methanolic extract’s absorbance was measured at 725 nm, and the number of entire phenolics was calculated in terms of OD units. gdw content .−1 in Bhilwara, Kota, Jhalawar, Pratapgarh, Bhopal, Meerut, Banasthali, and Bangalore respectively. The total phenolics in the samples from the dry sub-humid region were 10.01, 11.12, 9.96, and 15.11 OD units.gdw−1 in Saharanpur, Kanpur, Lucknow, and Varanasi, respectively. In the moist sub-humid region samples, the phenolic content was higher, i.e., 9.36, 10.62, and 11.77 OD units.gdw−1 in Pantnagar, Nazibabad, and Roorkee, respectively. The polyphenol levels were particularly low in Pratapgarh, where total phenolics were only 7.65 OD units.gdw−1. Thus, substantial differences in total phenolics between diverse accessions of S. cumini were observed, although the variations within various regions were relatively low. The dry sub-humid region showed a relatively broader range of 9.96–15.11 OD units.gdw−1 in total phenolics. The distribution of total phenolics among 16 S. cumini accessions revealed that the arid zone had the highest concentration of phenolics, followed by moist sub-humid, semi-arid, and dry sub-humid regions. The varying amount of total phenolics from the leaves of S. cumini may be a function of the vegetational stage, the geographic position of plants, and climatic circumstances [The total phenolics in the samples that belonged to semi-arid regions were 8.77, 7.38, 9.04, 7.65, 10.57, 11.74, 12.75, 11.25 OD units.gdwmstances ,36.Out of various tested solvent systems, 1, 5, 8, and 9 separated the standards of five phenolic acids, viz. ferulic acid, caffeic acid, sinapic acid, o-coumaric acid, and p-coumaric acid. The typical phenolic acid combination was divided into solvent system-1, which included benzene, acetic acid, and water, out of all the examined solvent systems (37:45:18). 3, a blackish-green and yellow spot was distinctly observed in S. cumini. P-coumaric acid and caffeic acid were separated in solvent 8. Consequently, in the qualitative analysis of total phenolics using preparative TLC, only caffeic acid and p-coumaric acid were tentatively recognized in around accessions of S. cumini samples.Based on the better resolution, the samples were permissible to run in solvent systems 1 and 8. After treatment with FeClS. cumini. The HPLC chromatograms of the five phenolic acids  and standard mixtures were monitored at 320 nm  were tentatively identified as ferulic acid, p-coumaric, and caffeic acid, respectively. For further confirmation, the sample from Varanasi and Jhalawar was co-chromatographed with caffeic acid and ferulic acid, respectively. The co-chromatography with authentic phenolic acid showed that the chromatograms’ peaks were separated with a simultaneous increase in the integration area value. Moreover, a significant amount of other phenolic compounds was also present, but these could not be further identified.−1) is concise in −1 in Bhilwara to 7.14 mg.gdwt−1 in Lucknow, was present in all samples. The p-coumaric acid varied from 0.02 mg.gdwt−1 in Bhilwara to 0.26 mg.gdwt−1 in Jodhpur and was also present in all samples of S. cumini. On the contrary, tentatively identified ferulic acid was present only in some samples and ranged from a lower amount of 0.04 mg.gdwt−1 in Jodhpur to a higher amount of 2.31 mg.gdwt−1 in Roorkee. The ferulic acid yield was exceptionally high in Roorkee.The concentration of each compound (identified/unidentified) was calculated based on peak area. Detailed quantification of diverse phenolic compounds (mg.gdwt−1 in Bhilwara (semi-arid) to 6.63 mg.gdwt−1 in Nazibabad (moist sub-humid). The peak 4 of the unknown compound originated at RT 5.5 and was enormous and ranged from 0.58 mg.gdwt−1 in Bhilwara (semi-arid) to 6.26 mg.gdwt−1 in Nazibabad (moist sub-humid). These two compounds were present in all accessions of S. cumini. Moreover, the other unidentified compounds as peak numbers 1, 2, 5, 6, 7, 9, 10, 11, 12  were also observed in relatively lower but significant amount and were not presented in all samples.Based on their quantity, peaks 3 and 4 (RT 4.4 and 5.5) gave major and clear peaks of polyphenols with a relatively much higher percentage of area. The amount of unidentified compound as peak number 3 varied from 0.54 mg.gdwt−1 and 0.77 mg.g−1, respectively, was reported in Eucalyptus uropendis [−1) were reported in aromatic herbs [S. cumini.Phenolic acids commonly found in wild medicinal plants are mainly caffeic acid, ferulic acid, p-coumaric, and sinapic acid. A comparable amount of caffeic acid and p-coumaric acid, 0.22 mg.gropendis . A studyropendis . On the ic herbs . Similaric herbs . Table 1S. cumini from different geographic areas may be used in the manufacturing of phytomedicine for predicting phytochemical content in cultivar development. Moreover, it would also be helpful for plant breeding, quality control, and intellectual property rights (IPR).A helpful technique for categorization of the ”polyphenolic fingerprint” could be used . It is cThe focus of this study was to check the correlation of molecular and phytochemical characters at the intraspecific level. Phytochemical profiling was employed as the base marker for chemotypic clustering. The dendrogram based on chemical profiling is shown in Cymbopogon sp. [Vitex rotundifolia population from different locations in China [Hypericum species from Serbia [A study conducted in 2005 also could not correlate the genetic dendrogram with that based on essential oil content in ogon sp. . This isogon sp. , whereasin China . In 2006m Serbia .S. cumini populations. Analysis of genetic profiles, the type and content of polyphenols produced in each of the accessions of S. cumini could constitute valuable tools for analyzing this specie’s biodiversity. Since these analyses successfully discriminate between the accessions of this species, they constitute interesting tools to select those accessions with the potential to be used for specific crop improvement programs. We explored the use of these analyses performed on different accessions of the species collected from geographically distinct areas of India. Our findings can be used for the commercial production and germplasm management of this medicinal plant.The presented data imply that the morphotypes are of multiple origins or due to different ecological growing conditions rather than genetically determined and that phytochemical races are induced by a limited number of genetic differences, which might have occurred independently in different S. cumini accessions. The most effective accessions for improvement projects were identified using the ISSR marker system and the study of commercially significant phenolic acids. These accessions might be employed as acceptable feedstock for various commercial uses.It has been well-documented that geographical conditions may affect the phytochemical profile of the plant. In addition to genetic research, phytochemical differences are investigated as a method for evaluating biodiversity and authenticating germplasm. This investigation revealed a large amount of variation across


Balaena mysticetus) was hunted to near extinction and remains Endangered on the International Union of Conservation of Nature Red List. The intense, temporally extensive hunting pressure may have left the population vulnerable to other perturbations, such as environmental change. However, the lack of genomic baseline data renders it difficult to evaluate the impacts of various potential stressors on this stock. Twelve EGSB bowhead whales sampled in 2017/2018 were re-sequenced and mapped to a previously published draft genome. All individuals were unrelated and void of significant signs of inbreeding, with similar observed and expected homo- and heterozygosity levels. Despite the small population size, mean autosome-wide heterozygosity was 0.00102, which is higher than that of most mammals for which comparable estimates are calculated using the same parameters, and three times higher than a conspecific individual from the Eastern-Canada-West-Greenland bowhead whale stock. Demographic history analyses indicated a continual decrease of Ne from ca. 1.5 million to ca. 250,000 years ago, followed by a slight increase until ca. 100,000 years ago, followed by a rapid decrease in Ne between 50,000 and 10,000 years ago. These estimates are lower than previously suggested based on mitochondrial DNA, but suggested demographic patterns over time are similar.The East Greenland-Svalbard-Barents Sea (EGSB) bowhead whale stock ( Balaena mysticetus) is the only baleen whale that lives its entire life in Arctic and subarctic regions, often in association with sea ice1. Four bowhead whale stocks are currently recognized: (1) the Bering/Chukchi/Beaufort Seas (BCB) stock; (2) the Okhotsk Sea (OKH) stock; (3) the Eastern Canada-West Greenland (ECWG) stock; and (4) the East Greenland-Svalbard-Barents Sea (EGSB) stock, earlier referred to as the Spitsbergen stock2. The bowhead whale, as a species, is listed as Least Concern in the International Union of Conservation of Nature (IUCN) Red List. However, the EGSB stock, which is distributed from the East Greenland Sea across the northern Barents Region into Russia (including Severnaya Zemlya and Franz Josef Land waters), is classified as Endangered2.The bowhead whale  from the Russian border westward to the northwest corner of the Svalbard Archipelago estimated some 350 individuals in this region in late summer11. Surveys conducted in East Greenland waters in spring and late summer/early fall documented similar numbers in the western parts of the stock’s range12. However, individual animals move across the whole range; bowhead whales tagged in East Greenland shelf waters have travelled eastward to Franz Josef Land and beyond1.There has been ongoing discussion regarding the census size of the EGSB stock. This population was thought to be large prior to the onset of extensive hunting, which commenced circa 1611. Estimates of the preharvest stock size range from 25,000 to 100,000 individuals17. For the EGSB stock specifically, four studies, all targeting mitochondrial markers, have been conducted. Borge et al.14 assessed mitochondrial haplotype diversity by sequencing parts of the mitochondrial D-loop region in bone remains from 99 historic bowhead whales. They found relatively high genetic diversity with levels similar to the genetic diversity of the extant BCB stock, which has also been depleted by extensive hunting. Three further studies generated full mitochondrial genome sequences from historic18 and contemporary individuals19. The contemporary mitochondrial genome sequences supported the earlier findings of Borge et al.14 that the EGBS population is genetically differentiated from the other three stocks. Inferences regarding the demographic history of the EGSB bowhead whale stock suggested a boom–bust scenario that included Late Pleistocene population growth 25,000–35,000 years ago, followed by a Holocene decline17. However, the confidence limits of the estimates were broad and overlapping throughout the time frame of the study, making it impossible to define numerical trends for this population. Since mitochondrial DNA is a single, maternally inherited, non-recombining marker, and thus particularly sensitive to drift in small populations, inferences based on just mitochondrial DNA have to be interpreted cautiously, as the data may not fully represent the diversity and demography of the EGSB stock. Accordingly, it is important to obtain nuclear genomic data that offer more refined insights.Several genetic studies have focussed on elucidating the genetic structure and differentiation of the four recognized bowhead whale stocks17. Nuclear high-throughput sequencing data were analysed for heterozygosity, population structure, inbreeding and demography. The heterozygosity estimates were compared to those obtained for archived sequence data for a conspecific individual from the ECWG stock and a North Atlantic right whale . These genomes and the findings provide important baseline data for future conservation and management of bowhead whale stocks.The current study addresses levels of genetic diversity in the EGSB stock of bowhead whales based on nuclear genomes from the 12 individuals that had been previously analysed using mitochondrial genomeshttps://arriveguidelines.org) for the reporting of animal experiments.Permits for animal handling were issued by the Norwegian Animal Research Authority , the Governor of Svalbard  and the Greenland Ministry of Fisheries, Hunting and Agriculture . All methods were carried out in accordance with relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (.17). The sampling sites are depicted in Fig. .17, GenBank Bioproject: PRJNA798027).Twelve bowhead whale skin biopsies collected during field expeditions in 2017 (ten samples) and 2018 (two samples) using a crossbow and custom-made darts were used in this study  following the Tissue DNA-Spin Protocol provided with the kit. Building of Illumina sequencing libraries and sequenced using 150 bp paired-end read chemistry on an Illumina NextSeq 500 was outsourced to StarSEQ GmbH, Mainz, Germany. The raw reads have been deposited in NCBI's Sequence Read Archive , an adaptor overlap of 1 (-O 1) and a quality cut-off of 20 (-q 20). Filtered reads were aligned to the Balaena mysticetus draft genome22 using the BWA-mem algorithm23. An overview of the mapping statistics and depth is provided in Supplementary Table http://broadinstitute.github.io/picard/). The GATK v3.7 (https://gatk.broadinstitute.org/hc/en-us) RealignerTargetCreator and subsequently IndelRealigner tools were used to remove PCR-duplicates and to account for insertion-deletion (indels) polymorphisms, which can affect downstream variant calling. Variant calling was performed both for variant and invariant sites using the GATK v3.7 HaplotypeCaller tool, resulting in a gVCF  for each sample. gVCFs were then merged using the GenotypeGVCFs tool, generating a raw VCF file for downstream analyses. This raw VCF file was then filtered for QualByDepth (QD) < 5.0, FisherStrand (FS) > 10.0, RMSMappingQuality < 40.0, MQRankSum < -2.5, and ReadPosRankSum < -2.5. Finally, to include only single nucleotide polymorphism (SNP) variants, the VariantFiltration tool included in GATK and vcftools v0.1.1324 was used.This study aimed at a sequencing depth of > 10 for the 12 bowhead whale samples in order to allow for reliable estimates of genetic diversity. Raw reads were quality checked using FASTQC v0.11.824. For the phylogenetic network analysis, the VCF file was converted into phylip format using vcf2phylip v2.025 and the analysis was performed using Splitstree v426. For the PCA, a linkage disequilibrium (LD) filter was applied with a window size of 50,000 bp and a step size of 10,000 (defining a r2 threshold of 0.18) using plink v1.90b5.227. This high-quality, unlinked set of variants was loaded into R v3.628 using the vcfR package29, and PCA was performed using adegenet30.A Principal Component Analysis (PCA) and a phylogenetic network were computed from the above variant calls to address population structure, branching pattern and the distribution of individuals within the population. The VCF file generated by the the GenotypeGVCFs tool was further filtered to remove SNPs with a minimum allele frequency < 0.05 and a mean depth value  using vcftools v0.1.1324. Significance of the differences between the observed and expected homozygous values was tested using a Welch two sample t-test. Additionally, individual inbreeding (F) and relatedness (R) levels between samples were estimated using NGSrelate v2, which uses identity by descent to calculate these metrics31. In contrast to vcftools, this method does not rely on directly calling genotypes, but instead gives the likelihood that any of the four nucleotides could be called at a certain position. Therefore, this method can also produce reliable results for low-coverage data (down to ca. 1x). Furthermore, NGSrelate relies on a population-level reference panel to compute relatedness and inbreeding, the more individuals included in the analysis—the more reliable the results are. Thus, for the relatedness analysis, the seven samples collected in 2006 and 2010 (previously sequenced to low coverage (0.8× to 2.45×), for sample details see SRA; Bioproject: PRJNA798027; accessions listed in Supplementary Table .19) were also included. As input for NGSrelate, genotype likelihoods (GL) of the dataset were calculated from the mapped bam files using ANGSD v0.92132. The following filtering options were used: minimum mapping and base quality of 30 (-minmapQ and -minQ 30); calculate genotype likelihoods using the GATK algorithm (-GL 2); output binary genotype likelihoods (-doGlf 3); infer major and minor alleles using GL (-doMajorMinor 1); calculate per site frequencies using a fixed major and unknown minor allele (-doMaf 2); only include SNPs with a p-value less than 1 × 10–6 , and a minimum minor allele frequency of 0.05 (-minmaf 0.05). The number of sites included in the pairwise comparisons between individuals to calculate relatedness ranged from 2,118,337 to 12,161,246. Further, runs of homozygosity (ROH) as a proxy for inbreeding were determined in each of the 12 newly generated genomes (samples collected in 2017 and 2018) using ROHan33. The program was run using only autosomal scaffolds > 1 Mb in length to avoid biases due to the fragmented assembly. However, it should be noted that although the analysis still contained 968,853,266 bp of data, ROH may be downwardly biased from the fragmented assembly. ROHan was run twice, once specifying a ROH if 1 Mb of data contains an average proportion of heterozygous sites less than 1 × 10–5 and once specifying the average proportion of heterozygous sites less than 5 × 10–5. Bowhead whale X and Y-linked scaffolds were identified by aligning the published bowhead whale genome assembly to the cow X chromosome (GenBank accession number: AC_000187.1) and the human Y chromosome (GenBank accession number: NC_000024.10) using satsuma synteny34. The latter were subsequently removed because they can bias heterozygosity estimates in males.Observed and expected numbers of homozygous sites were determined from the GATK filtered variants using vcftools (–het)35 in order to avoid coverage linked biases and to ensure comparability between previous estimates. Genetic diversity estimates were subsequently determined using ANGSD v0.92132 with the filtering steps following Westbury et al.36 and only compared against estimates from other species calculated using the same software and parameters. The filtering parameters were as follows: calculated genotype likelihoods using the SAMtools algorithm (-GL 1); only consider sites with at least 5 × coverage (-setMinDepthInd 5); minimum mapping and quality scores were defined as 25 (-minmapq 25 and -minq 25); only consider reads mapping to a single region uniquely (-uniqueonly 1); remove secondary alignments (-remove_bads 1); calculate allele frequencies (-doSaf 1); only use scaffolds > 100 kb (-rf); perform indel realignments (-baq 1); and do not specify an ancestral allele (-fold 1). The genomic raw reads (GenBank biosample ID: SAMN14122067) of the North Atlantic right whale  were downloaded and mapped to a North Atlantic right whale reference genome retrieved from https://www.dnazoo.org/assemblies/Eubalaena_glacialis using BWA mem and default parameters. From a total of 773,071,534 reads  596,115,335 resulted in a mean read depth of 38.8x. Autosome-wide heterozygosity for the right whale was calculated using the same approach as described above for the bowhead whales.Autosome-wide heterozygosity was calculated for the seven samples with > 20 × average read depth (Supplementary Table Ne) through time) were run on each individual using a Pairwise Sequentially Markovian Coalescent (PSMC) model37. As input for PSMC, indel-realigned diploid consensus sequences were built from the mapped bam files using SAMtools35 and BCFtools (http://samtools.github.io/bcftools/bcftools.html). The atomic time interval setting 4 + 25*2 + 4 + 6 was used38. Mutation rate was set to 2.69 × 10–8 substitutions per generation (i.e. 7.69 × 10–10 per site per year), a value specifically calculated for bowhead whales by Westbury et al.38. Following Rooney et al.13 the generation time was set to 35 years, an estimate that is based on information about lifespan, sexual maturity and reproduction intervals of female bowhead whales. However, generation time of bowhead whales is a topic of debate, and estimates may be considered 'guesstimates'. Other studies40 have used ca 50 years, and we repeated the analyses with a generation time of 50 years. Different generation times will affect the timelines of demographic analyses but not the overall trajectories. To estimate the more recent demographic history of the EGSB stock stairway plots v241 was implemented using the unfolded site frequency spectrum (SFS) calculated using the 12 newly generated genomes (samples collected in 2017 and 2018). The right whale was mapped to the bowhead whale reference genome using BWA mem to determine the ancestral state. A total of 542,282,179 reads mapped, resulting in a mean read depth of 38.8x. The SFS was calculated from allele frequencies in ANGSD v0.92132 (-doSaf 1) from all scaffolds > 100 kb in length with the following parameters: -minmapQ 25, -minQ 25; -GL 2, -doMajorMinor 1; -doMaf 2, only use sites where all individuals have coverage (-minind 12); skip triallelic sites ; and -setMinDepthInd 5. The same generation times and mutation rate were used as for the PSMC analyses. An analysis using a folded SFS was also run to investigate whether the ancestral state could impact the results. ANGSD was run in the same fashion as for the unfolded SFS but with the -fold option and the -anc sequence replaced with the bowhead whale reference genome instead of the right whale.Deep time demographic analyses . The NGSrelate inbreeding coefficient for the 12 individuals was 0. ROHan did not find any significant ROH in any of the 12 individuals when using the default ROHan setting of 1 × 10–5 for the definition of a ROH. However, when increasing this to 5 × 10–5 there was some ROH in all individuals except for 17–18 and 17–19  ranged from 0.00098 to 0.00105 Table  revealedNe) from ca. 30,000 individuals some 1.5 million years ago to ca. 7000 individuals ca. 250,000 years ago. Subsequently, there was a slight increase of Ne up to ca. 9000 at 75,000 to 100,000 years ago. Figure Ne was estimated as more or less stable between 20,000 and 30,000 individuals , followed by a stable Ne until around 50,000 years ago before a dramatic decrease between 50,000 and 20,000 years ago. The plots were similar when using both the folded and unfolded SFS as inputs  have been a concern raised by both researchers and conservation and management bodies such as the International Whaling Commission (IWC) and the IUCN43. However, the bowhead whale is a long-lived mammal, and its longevity may extend past 200 years48. Even if hunting was extreme, the effective population size would have needed to be extremely small over the entire period of hunting to observe substantial reductions in genetic diversity in the present. The findings were somewhat different for six (samples A–F) of the seven individuals sampled in 2006, which showed very high relatedness values. However, as has been speculated earlier19, this 2006 collection is likely biased; all samples were collected within a very small area on the same day from a group of bowhead whales that were moving in synchrony8. Accordingly, re-sampling of the same individual may best explain the finding of identical mitochondrial genome sequences for the six individuals in question18. Importantly, the low coverage data for the samples collected in 2006 and 2010 were only used for the relatedness analysis; accordingly, the most likely duplicate samples did not bias any other analyses.Based on the analyses reported here, there are only a few significant runs of homozygosity (ROH) for the 12 individuals sampled in 2017 and 2018. Furthermore, autosomal heterozygosity proportion estimates for seven bowhead whales sampled in 2017/18 Fig.  were rel11. It must be noted, however, that this estimate only represents part of the stock’s range, and the genomes of the sequenced individuals are highly diverse genetically speaking. Of note, autosomal heterozygosity levels were similarly high in all seven bowhead whale individuals analysed in this context. However, high genetic diversity does not always translate to large population size. Factors such as changes in population size, gene flow or inbreeding levels can also strongly affect the estimates. For various remnant populations of Cape buffalos (Syncerus caffer) and Giant panda (Ailuropoda melanoleuca), for example, relatively high genome-wide heterozygosity has been reported while population sizes are known to be low49. This may not be surprising because the population decline is recent in these cases. However, in other Arctic whale species such as the beluga whale (Delphinapterus leucas) and narwhal (Monodon monoceros), which both have a substantially larger population sizes, lower mean autosomal heterozygosity than the average EGSB bowhead whale value derived herein have been observed; for the narwhal, a recent rapid expansion from a much smaller founding population has been suggested as a likely driver of the low genetic diversity found for this species38. Unfortunately, except for the one ECWG bowhead whale individual sequenced by Keane et al.22, there are to date no other genome-wide estimates for individuals from other bowhead whale stocks. As pointed out above, it remains open as to what extent the estimates presented here reflect genetic diversity of bowhead whales across the entire distribution range of the species; though BCB whales have also retained high levels of diversity despite heavy historical harvests. Previous studies on mitochondrial genome diversity of contemporary EGSB stock individuals also indicated relatively high levels of genetic diversity (haplotype diversity (HD): 0.858, nucleotide diversity (π): 0.0027)19. Yet, continued very small population size, or even further decline in population size would mean an increasingly rapid accumulation of inbreeding and inbreeding depression.Low levels of relatedness between individuals and ROH, in addition to high levels of autosomal heterozygosity proportions, is often indicative of large population size. The most recent population estimate of ~ 350 individuals for EGSB bowheads, which is based on a combined helicopter- and ship-based line transect survey from the Russian border westward to the northwest corner of the Svalbard Archipelago stock, is still rather low51. Heide-Jørgensen et al.52 conclude that the waters around Franz Josef Land may be occupied by at least a hundred EGSB bowhead whales. Further east, bowhead whales have also been observed in the western Laptev Sea53, and the north of the Novosibirskie Islands Archipelago, which separates the Laptev and East Siberian seas54. Bowhead whales from the Bering-Chukchi-Beaufort (BCB) population are also believed to occasionally penetrate the East Siberian Sea, at least to 170° E longitude55. It is thus possible that individuals from the EGSB and BCB populations may meet in the Russian Arctic seas52. Briefly summarized, the knowledge on the current population size of the EGSB stock remains limited. The survey-based estimates of several hundred individuals are preliminary and further information is needed. The estimates of genetic diversity and an effective population size Ne in the low ten-thousands in the Pleistocene provide important information regarding EGSB stock size.When attempting to interpret the genetic diversity data in light of census and/or stock size of the EGSB stock, it has to be noted that census size estimates to date do not cover the entire range of this stock (see above). It is very unlikely that the stock consists of just one aggregation of animals moving as a group across the range. Rather, the stock probably segregates seasonally, and observers in different parts of the range see different feeding aggregations over the Arctic summer. Particularly little is known about bowhead whales in the western Russian Arctic, although bowheads are thought to reside in the Franz Josef Land region year-roundNe) of the EGSB stock. They revealed a long-term (past ca. 1.5 M years), gradual decline in Ne, a pattern similar to what has been reported for other baleen whale species56. Several fluctuations were also uncovered in the past 0.5 M years. In contrast, demographic analyses based on mitochondrial genomes from the same bowhead whale individuals sampled in 2017 and 2018 revealed a higher female effective population size ) during the Pleistocene, ranging from ~ 70,000 to ~ 110,000 individuals17. However, as with genetic diversity estimates, estimates of Ne must be interpreted with caution; importantly, numerical estimates from different studies that are based on different genetic markers  cannot be directly compared. Earlier studies have reported that the relative nuclear diversity of cetacean species is correlated with population size, while mitochondrial diversity is not59. For mitochondrial diversity, social structure and matrilineal social systems were emphasized as important drivers of range-wide and regional mitochondrial genetic diversity. Despite all these uncertainties, the mitochondrial genome analyses also supported an increase in Ne for EGBS bowhead whales during the late Pleistocene17, in agreement with the demographic pattern obtained in this study with nuclear genome data.The demographic analyses of the genome data shed some light on the demographic trajectories through time of effective population size (Ne depend heavily on key parameters such as generation time and mutation rate as well as assumptions on gene flow. Accordingly, they cannot be considered exact values and must be interpreted with caution. The mutation rate of 7.69 × 10–10 substitutions per site per year for nuclear genomic DNA, which was used in the present study, and which was estimated specifically for bowheads36, may be considered low. It is substantially lower than the average mammalian genome mutation rate of 2.23 × 10–9 per site per year suggested by Kumar & Subramanian60. However, this discrepancy is not surprising, as the latter estimate was not based on genome-wide data, but rather on the analyses of some protein coding genes. The mutation rate used in the present study is in line with reduced substitution rates observed within Cetacea61, and is similar to the average nuclear mutation rate of 4.5 × 10–10 per site per year calculated by Jackson et al.62 for baleen whales. Although using another mutation rate would have resulted in different numerical estimates of Ne, the order of magnitude of change in effective population size for the late Pleistocene EGSB stock estimated in the current study lies within the range of census size estimates based on information from other sources, for example, 25,000–100,000 individuals as suggested by Allen & Keay3. The extent of gene flow between stocks is a further uncertainty for estimates of Ne. Given the long period of several millions of years encompassed by the demographic analysis, it is possible that free migration between stocks might have been possible in periods with minimal sea ice, as well as significant changes in ranges. Fossils of bowhead whales have, for example, been discovered along the Norwegian, Swedish and Danish coasts64, well south of the species' current distribution, indicating range shifts over time in response to climate change.Estimates of 58, small, but significant genetic differentiation between the four recognized stocks have been observed65. This study provides the first nuclear genomic baseline data set for the EGSB stock and reveals high genetic diversity in bowhead whales in this region. This information calls for more comprehensive analyses of genetic differentiation and for the reconstruction of the demographic history of bowhead whale stocks in the future.Genetic differentiation and population sizes of historic and contemporary bowhead whale populations have been the subject of considerable research effort. Most molecular studies on these topics have used relatively short mitochondrial D-loop sequences to address levels of differentiation. Despite high rates of gene-flow between the EGSB, ECWG and BCB populations in the recent pastSupplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


As climate change increasingly threatens agricultural production, expanding genetic diversity in crops is an important strategy for climate resilience in many agricultural contexts. In this Essay, we explore the potential of crop biotechnology to contribute to this diversification, especially in industrialized systems, by using historical perspectives to frame the current dialogue surrounding recent innovations in gene editing. We unearth comments about the possibility of enhancing crop diversity made by ambitious scientists in the early days of recombinant DNA and follow the implementation of this technology, which has not generated the diversification some anticipated. We then turn to recent claims about the promise of gene editing tools with respect to this same goal. We encourage researchers and other stakeholders to engage in activities beyond the laboratory if they hope to see what is technologically possible translated into practice at this critical point in agricultural transformation. Will gene editing contribute to improved crop diversity and climate resilience? In this Essay, the authors look at lessons from past biotechnology efforts to inform action for the future. In 1970, a virulent fungal blight decimated the United States corn harvest. This southern corn leaf blight epidemic was linked to a subset of genes that made certain varieties more susceptible than others—genes that also happened to be shared across some 75% of commercial varieties . The bliWith these concerns as motivation, some researchers pointed to the possibility of increasing genetic diversity among cultivars of a given crop with a brand-new biotechnology: recombinant DNA . These tThe need to diversify crops is coming back into focus due to increasingly urgent climate and nutrition challenges –9. DiverBiotechnology is once again offering a path forward. Today’s plant scientists are developing gene editing techniques that could facilitate genetic diversification of commodities like wheat, rice, and maize and potentially support the adoption or continued cultivation of “neglected” crops that have been less often subject to crop breeding and development activities. But will gene editing really generate a diversity boom? Can it upend a pattern of genetic narrowing that breeders and botanists have observed since the late 19th century—a pattern frequently pinpointed as a major source of vulnerability in global agricultural production systems?Excavating comments that reveal an often forgotten subset of early aspirations for recombinant DNA technologies provides insight on contemporary dialogue about gene editing. The history of these technologies illustrates the extent to which diversification depends on much more than a laboratory toolkit. Awareness of past efforts can inform today’s aspirations for and decision-making about the use of crop biotechnologies to enhance genetic diversity.The turn of the 20th century saw the emergence and rapid development of genetics as a research discipline and with it the celebration of specific knowledge and tools as novel means of controlling the heredity of plants, animals, and humans. The “rediscovery” of Gregor Mendel’s studies of inheritance in peas in 1900 provided ambitious discipline builders with a simple framework for explaining the transmission of traits and enabled many to recruit further resources for research ,12. AlthEarly breeders-turned-geneticists typically deployed the well-established approaches of hybridization and selection in their efforts to make new varieties “to order” and were able to generate and market new seed products as a result. Many were nonetheless already experimenting with tools that allowed direct physical manipulation of genes and chromosomes by the 1910s, hoping that these would open further horizons for plant breeding . ExposinThen a new set of tools arrived, technologies allowing genes from one organism to be inserted into the genome of another, even across species boundaries. Where the “mutation breeders” of the mid-20th century envisioned speeding up evolution, some molecular biologists of the 1970s saw themselves abandoning evolution altogether as they generated “transgenic” organisms . Crop brProcesses by which the reproduction of a plant species comes to be under the control of human groups, typically so that it can be used for food, clothing, shelter, or other purposes.A plant species closely related to a domesticated agricultural crop, for example, an evolutionary ancestor or related taxon.A locally adapted plant variety or animal breed maintained as a distinct population.A crop species that has not been subject to significant research for commercial development and is therefore potentially underused in agriculture.Methods that rely on crossing together plants via sexual reproduction.Techniques that use chemical or radiation treatment to induce random changes to a plant’s DNA.Molecular biology techniques that facilitate combining genetic material from multiple sources.The insertion of DNA from one organism into the genome of another unrelated organism. For crops, this involves isolating genetic material from an organism like a bacterium or a different plant species and transferring it into the crop genome.Similar to transgenesis, but instead of the inserted DNA coming from an unrelated organism, it originates in a related plant, such as a crop wild relative.A range of techniques that induce targeted genetic changes. Many products generated via gene editing only contain mutations in the crop plant’s own genome instead of insertion of DNA from an external source.For almost a hundred years, experts had insisted that as farmers adopted increasingly uniform “modern” varieties produced by professional breeders, the more heterogeneous varieties previously maintained by farmers would vanish ,19. And The loss of genetic diversity associated with the transition from varied and genetically heterogeneous farmers’ varieties to breeders’ more standardized products at ever-increasing scales became a crisis scenario for many botanists and crop scientists in the 1970s, and not just because of the US corn blight. A Green Revolution in agricultural production, in which farmers of South Asia, Latin America, and the Middle East were thought to be transitioning en masse to “modern” varieties, was first declared in 1968 and swiftly identified as a potential diversity bottleneck .Green Revolution varieties, such as the semidwarf wheat first developed in Mexico and the semidwarf rice IR-8 bred in the Philippines, had complex genetic origins and were celebrated in particular for their ability to survive in diverse ecological contexts given the right inputs. The dissemination of these new varieties, along with increased availability of nitrogen fertilizers, irrigation, and other inputs, led to significant increases in grain production in many places and to a drop in food prices .As many observers noted, then and since, these gains in yield were accompanied by several social and environmental costs. One consequence of the widespread adoption of these “miracle varieties” ,28 was aMeanwhile, global population growth and concerns about resource scarcity, factors that had spurred the crop research associated with the Green Revolution, also focused international attention on many other kinds of biological diversity. The loss of this diversity, whether represented in genes, species, or ecosystems, spurred new conservation activities .Set in this context, circumventing evolution through transgenic engineering was celebrated by some plant scientists as a contribution to conserving or even amplifying genetic diversity, especially in industrial crops. Ambitious researchers, along with eager agricultural administrators and policy-makers, imagined screening the world’s biodiversity, identifying genes of interest, and transferring them into valuable lines. They hoped the result would be an unprecedented mobilization of genetic material to create diversity not known or even imaginable in nature .Although enhancing genetic diversity in industrial crops was never the most prominent goal sought by researchers working with recombinant DNA in crop development, this potential outcome surfaced often in conference proceedings and other forums where technical workers came together to project the future of their field. Among the most frequently promised diversifications were grains with enhanced protein content, an expanded range of agricultural crops able to fix nitrogen, and modern cultivars with the traits for heat- and drought-resistance possessed by their wild and weedy relatives or landraces stored ex situ in seed and gene bank collections ,30–32.Recombinant DNA techniques were sometimes touted not only as making seed and gene bank collections more valuable, facilitating breeders’ use of stored genetic diversity, but also as instruments for focusing public attention on the importance of biodiversity conservation . The motBacillus thuringiensis (Bt) that result in insect-resistance [In retrospect, these hopes that transgenic tools would be a boon for biodiversity seem misguided. The vast majority of transgenic crop varieties cultivated today possess only 2 engineered traits. They either contain 1 or several genes derived from sistance  or they sistance . Increassistance . In 2019sistance .The deployment of these crops has been controversial, and researchers are continually exploring whether and under what circumstances their use enhances agricultural productivity and sustainability –42. One Some transgenic crops possess traits other than herbicide tolerance and Bt. One example is Golden Rice, generated as a tool to address the public health concern of Vitamin A deficiency that affects primarily women and children. Through the incorporation of 2 genes from other species, scientists were able to enrich the rice grains with beta-carotene, a precursor of Vitamin A . In deveAnother transgenic crop in cultivation is the Rainbow papaya, which resists the devastating papaya ringspot virus thanks to insertion of a gene from a weak strain of the virus itself ,51. ThesFurther traits are in the pipeline, many of which are derived from wild relatives of crop plants. The Cavendish banana makes up almost 50% of the world’s banana production  and is dMeanwhile, in the United Kingdom, scientists have introduced 3 genes from potato relatives into a cultivated variety to confer resistance to late blight and tuber blight—diseases notoriously catastrophic for growers. It is expected that the stacking of these multiple wild resistance genes will provide more robust disease resistance than previous conventional breeding efforts because it will be more difficult for the pathogen to adapt to the plants’ defenses ,56.Even considering these efforts, there is an undeniable disconnect between what was imagined with respect to diversifying agricultural crops through genetic engineering and the reality that unfolded. For many reasons (discussed further below), the opportunities created by recombinant DNA technology with respect to crop diversity remain unfulfilled in practice.Leading plant scientists today praise innovative gene editing techniques as game-changing methods destined to fulfill aspirations for expanding crop genetic diversity through biotechnology –60. ThisOne key difference between gene editing and recombinant DNA lies in the nature of the genetic diversity being explored. Instead of mining gene bank collections or searching the genomes of other organisms, many efforts to expand genetic diversity with gene editing focus on “unlocking” the variation within a plant’s own genome. Whereas early recombinant DNA programs intended to defy evolution, these gene editing goals are reminiscent of the hopes of the mid-20th century mutation breeders: accelerating evolution. This time, however, instead of randomly generating variation with radiation treatment or mutagenic chemicals, the technology is able to deliver more precise and predictable genetic change ,65.Gene editing methods employing site-specific nucleases  are guided either by protein engineering or RNA sequence complementarity to target a specific location in an organism’s genome. These gene editing techniques, combined with the rapid advance of genetic sequencing technologies and digital analysis tools, can potentially be powerful instruments for increasing crop genetic diversity. A brief overview of several strategies that researchers currently imagine using to achieve this end indicates the range of possibilities.One strategy to increase genetic diversity using gene editing is to alter genes that confer undesirable traits, such as susceptibility to disease. For example, disrupting genes involved in plant susceptibility to powdery mildew in crops such as tomato, grapevine, and wheat induced resistance to the disease –68.In other cases, a sought-after trait is not associated with loss of a gene’s function but rather a change in the extent to which a certain gene is expressed. Gene editing can address these traits, too. Instead of targeting coding sequences, as in the case of powdery mildew, the editing can be directed to genetic regulatory elements, thereby altering the level of gene expression ,69. ThisGene editing also offers opportunities to radically rethink the breeding process in ways that enhance genetic diversity by “restarting” crop domestication. Crop domestication relies upon a combination of spontaneously occurring genetic mutations and artificial selection by humans. In wild rice, for example, grains shatter in order to widely disperse the seed. During rice domestication, a mutation arose that caused non-shattering grains, a trait beneficial for early agricultural societies and therefore selected for cultivation. Rice wild relatives today carry beneficial traits like adaptation to diverse growth environments but their grains still shatter.“De novo domestication” uses gene editing to generate mutations that are known to be markers of domestication in crop wild relatives or neglected crops. This “restarts” the domestication process, with the aim of reclaiming beneficial traits retained by these plants such as nutrient content or stress tolerance, while also enabling essential domesticated traits—like non-shattering grains. The technique has already been demonstrated in multiple plant species, including tomato, rice, and the neglected crop ground cherry –74. EditChromosome engineering via gene editing presents even more possibilities for increasing genetic variation –78. The As a final illustration, in an approach that seems to materialize the ambitions of mutation breeders past, gene editing can be used as a tool for “directed evolution,” a technique designed to accelerate the evolution of a specific protein. First, scientists create CRISPR libraries targeting every possible location in the gene of interest. The goal is to create random variation in the gene in the hopes of finding a mutation that leads to an optimized version of the gene’s protein product. After performing the gene editing step, mutants are screened for the desired enhanced phenotype ,80.Various technical obstacles to efficient gene editing in plants remain, like overcoming the bottleneck of plant tissue culture . But metHistorically, plant breeding in the hands of professional breeders, whether funded by state governments or private industry, has trended in the direction of narrowing crop diversity . As 20thIn the case of recombinant DNA technology, this question has been the subject of innumerable studies and much debate ,84. IssuThere is good reason to advocate for such changes, as the urgency of expanding crop genetic diversity has only intensified since the 1970s. From tolerating drought to fighting off pests to enabling healthy diets, increasing crop genetic diversity is an important strategy for ensuring climate resilience and meeting nutritional needs in many different farming contexts—from subsistence cultivation to industrial monocrop production . Not allYet history shows that laboratory achievements do not lead to promised crop diversification when other elements of agricultural research and production systems do not facilitate it. Do financial and institutional resources for research support crop development for diversity? Is funding in place to support projects that may not represent immediate economic gain? Are crop biotechnology regulations designed to promote development of diverse traits? Do varietal registration requirements encourage genetic diversity in varieties? Do farmers know how to manage a new variety? Are processors prepared to handle a different product? Are consumers ready to eat foods prepared from reintroduced “neglected” crops? All of these concerns arise in addition to the more often asked question of whether a particular community is willing to eat transgenic or gene-edited foods. An ever-growing number of tools to diversify crops are unlikely to become significant if they lack a pipeline that also facilitates diversification or fail to address the needs of growers and eaters in a wide variety of ecological and cultural contexts.It is tempting to view these considerations as factors “downstream” of the laboratory. Yet they are inextricably linked to all stages of applied research, from project funding to commercialization to social and environmental impact. Consider the 2018 European Court of Justice ruling that gene-edited products would be strictly regulated in the European Union . This deCrop biotechnologists hoping to see their diversity-enhancing lines flourish in fields must therefore be ready to engage in discussions beyond laboratory walls. To forge a path forward, researchers can raise critical awareness and foster shared support among sectors for increasing crop genetic diversity to tackle urgent global challenges.Part of this effort will include clearly illustrating how biotechnology can play a meaningful role in this transformation. But researchers’ claims about what new biotechnologies can achieve must account for the inseparability of the technical and the sociopolitical. Past failures to recognize that outcomes are largely shaped by the expectations and decisions of farmers, consumers, and policymakers has led to erosion of public trust that continues to affect the acceptance of crop biotechnology ,100.Designing tools that enable researchers to understand and assess the effects of their efforts on desired agricultural transformations may aid in raising awareness . This woUsing biotechnology to expand crop genetic diversity will also require that researchers understand the many junctures in crop variety development and dissemination, especially those linked to seed commercialization, that work against such expansion . AddressOvercoming the long association of professional plant breeding with narrowing genetic diversity and the more recent restriction of biotechnological tools to the same fate is a daunting challenge. Farming systems that rely on commercial varieties produced by professional breeders will always see a genetic narrowing at some scale. But there are opportunities to remove or mitigate points at which significant bottlenecks occur even in these systems. Several scientists and research institutions are already paving the way forward. The OpenPlant initiative  in the UThis program’s focus on reducing intellectual property protections and therefore economic barriers to plant biotechnologies recognizes that the objectives embodied in new technologies, including crop varieties, typically reflect the goals of those holding the purse strings for technical development. The history reviewed above, from the Green Revolution of the 1960s to the gene revolution that followed, shows how multinational corporations and select philanthropies have played an outsized role in directing crop development in the past 70 years, and that this pattern of investment has not been conducive to producing or even sustaining diversity. Moving forward, the major actors involved and the priorities they set will determine how crop biotechnologies are used, and ultimately, what crops farmers choose to grow.For crop biotechnology to help achieve ambitious climate and nutrition goals, action needs to happen now. Researchers, farmers, industry leaders, policy-makers, and the public are all responsible for creating circumstances that promote greater crop diversity, instead of reinforcing patterns that extend uniformity. These efforts, technical and social, are essential to ensuring that promising biotechnology discoveries are effectively implemented to drive sustainable and equitable agriculture systems in the face of a rapidly changing climate.


M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode Caenorhabditis elegans that accumulated mutations in a nearly neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of C. elegans genotypes allows selection on new phenotypic dimensions of locomotion behavior.Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the  G matrix, the additive genetic (co)variance matrix summarizing how multiple traits are genetically structured and inherited from parents to offspring, provides both prospective and retrospective information about phenotypic evolution. Looking forward, the evolution of mean trait values over one generation, G and linkage disequilibrium (LD) generated by past selection  , where zM per generation, where the diagonal elements of M are the mutational variances, and the off-diagonals the mutational covariances between traits. In the long-term of mutation-drift equilibrium, G between populations is 2M per generation variance from a diploid population at rate ΔG=G will match that of M  consistent with mutation–selection balance predictions, reviewed in M was in some studies measured in different genotypes but effectively encompassing only a single “averaged” genetic background of a single population from a single species; see Discussion. How can genetic and phenotypic evolution be predictable in the very long term of thousands of generations if M is bound on the short term of tens to hundreds of generations to be subject to considerable sampling variance because mutations are rare events and all populations are finite? In addition, sampling effects will be compounded if mutation effects depend on genetic background and there is thus mutational “bias” due to epistastic interactions between the relevant quantitative trait loci (QTL) (M which in turn should affect standing levels in G and ultimately D (The deterministic model in equation  suggestshat of M . Two stuci (QTL) . If mutamately D .G before reaching mutation–selection balance one could start by finding out how variable is M between different genotypes, even if from a single population and species. As a second step, we can compare M with standing genetic (co)variances (G matrices) of populations evolving in the environment where phenotypic effects are measured. Using six independent traits of locomotion behavior as a model in the hermaphroditic nematode Caenorhabditis elegans, we here characterize the M matrix in two genotypes, using mutation accumulation (MA) experiments, and compare it with the G matrix of a lab-adapted population with standing genetic variation. Comparison of the two M matrices between founder genotypes allow us to characterize the degree of sampling and/or genetic background effects in de novo mutational input. In the short term of a few tens to hundreds of generations, and in the context of our particular lab environment, comparison of the M and G matrices allow us to question the relative role of pleiotropy due to mutation and LD due to selection in the maintenance of genetic (co)variances in locomotion behavior.To understand the evolution of the M matrices on locomotion behavior, we employed 250 generation mutation accumulation (MA) lines from two genotypes (N2 and PB306). The details of the derivation of these MA lines can be found in de novo mutations will be fixed within each lineage unless they are extremely deleterious  for 33 generations that was then followed by a domestication to a defined standard lab environment for 140 generations. This standard lab environment during lab evolution is the same as that used to measure locomotion behavior at the time of reproduction in both MA lines and the lab adapted population (see below). During domestication, the population was maintained in a 4-day discrete and non-overlapping life-cycle at We compared the f 60–80% . From A6Escherichia coli HT115). This occurred 2–3 generations after thawing, after which individuals were washed from plates in M9 buffer. Adults were removed by centrifugation, and two plates per line were seeded with 1,000 larvae each. Samples were then maintained for two complete generations in a common environment characterized by a 4-day non-overlapping life-cycle with extraction of embryos from adults done by “bleaching” and developmental synchronization of L1 larvae under starvation in M9 , a few only once (12) and 6 lines three times, as reported in We imaged adults using the Multi-Worm Tracker [MWT version 1.3.0; Q. The coefficients of Q matrix define the probabilities of observing a worm in state j while knowing its previous state i after a waiting time Q, noted i to state j . The 6 transition rates We estimated Models were fit with the R package MCMCglmm . We defiM matrices, we constructed 1,000 randomized M matrices to generate a null distribution. These randomized matrices were then used for the subsequent construction of null distributions (see next section). For this, we randomly shuffled MA line and block identities and fit the above equation to obtain 1,000 posterior distributions. As discussed in For each of the two M matrices of founder genotypes, we performed eigendecomposition of each N2 or PB306 M matrix independently. The resulting first eigenvectors usually contain most of the genetic variance due to mutation, the genetic variance that is measured by the eigenvalue To compare the M matrix and their eigenvalue M matrices, the angle will be higher than 45We then compared the relative direction of the phenotypic dimension with more genetic variance between N2 and PB306 by computing the angle between their respective mmax:Θ=180πcos matrix):M matrices.Additionally, we used a metric introduced by M matrices of the two inbred founders of lab evolution, N2 and PB306, with the G matrix of an outbred lab-adapted population A6140. G matrix estimation of A6140 is detailed in M matrices and the A6140 G matrix depends on many factors, such as mutation rate and effects, which determine the sampling of a finite number of mutations, given the number of generations of MA  or the extent of drift and selection experienced by A6140 during its history (see Introduction). Here, we want to compare the relative amount and orientation of genetic (co)variances within and between locomotion traits. To compare M and G matrices, they thus need to be standardized on a common phenotypic scale. For this, we computed three new matrices by scaling genetic (co)variances by the total phenotypic variation and centering transition rates to a mean of zero and then dividing by the mean standard deviation among transition rates. Specifically, for each of our three groups of inbred lines:We compared the M and G matrices, we performed eigentensor analysis  containing standing genetic variation (see Introduction and Methods). Detailed characterization of this G matrix has been presented in We compared the size and orientation of the N2 and PB306 M matrices of N2 and PB306 with the G matrix, they were standardized to a common phenotypic scale (see Methods). We find that leaving the still movement state (SF and SB transition rates) contains more standing than mutational genetic variances variance matrices reveals that only the first eigentensor is different from random expectations , and thactor e11 . The angmatrices . We alsoong gmax : PB306 sM matrix differs among genotypes within species is important to understand short- and long-term phenotypic evolution (see Introduction). Here, we have described the M matrix for six traits in locomotion behavior in two genotypes of C. elegans, after MA in a nearly neutral manner for 250 generations , the G matrix of the lab-adapted population was different from the M matrices of the two genotypes. The two kinds of M and G matrices are different due not only to more genetic (co)variances in the lab population between traits related to worm activity and leaving the still state but also because there was relatively less genetic variance created by mutation in leaving the still state than present in the lab-adapted population variances determines standing genetic (co)variances and, eventually, the phenotypic divergence between populations. While the study of jkac335_Supplementary_DataClick here for additional data file.