FST value of 0.08. We also reported QTL regions associated with forage biomass yield based on field phenotype data measured on a subset of the Napier grass collections. The findings of this study offer useful information for Napier grass breeding strategies, enhancement of genetic diversity, and provide a guide for the management and conservation of the collections.Genetic diversity amongst genotypes of several Napier grass collections was analyzed and compared with the diversity in a set of open pollinated progeny plants. A total of 114,881 SNP and 46,293 SilicoDArT genome-wide markers were generated on 574 Napier grass genotypes. Of these, 86% of the SNP and 66% of the SilicoDArT markers were mapped onto the fourteen chromosomes of the Napier grass genome. For genetic diversity analysis, a subset of highly polymorphic and informative SNP markers was filtered using genomic position information, a maximum of 10% missing values, a minimum minor allele frequency of 5%, and a maximum linkage-disequilibrium value of 0.5. Extensive genetic variation, with an average Nei’s genetic distance value of 0.23, was identified in the material. The genotypes clustered into three major and eleven sub-clusters with high levels of genetic variation contained both within (54%) and between (46%) clusters. However, we found that there was low to moderate genetic differentiation among the collections and that some overlap and redundancy occurred between collections. The progeny plants were genetically diverse and divergent from the germplasm collections, with an average  Cenchrus purpureus (Schumach.) Morrone syn. Pennisetum purpureum Schumach.), also known as elephant grass, is a perennial C4 grass belonging to the genus Cenchrus, one of the most diverse genera, consisting of about 140 species including important cultivated species such as Napier grass, pearl millet and kikuyu grass3. Napier grass is native to Sub-Saharan Africa and has been distributed to other tropical and subtropical regions around the world, adapting to a wide range of agro-ecologies, from sea level to 2500 m above sea level (m.a.s.l.). It has been acclimatized in areas of Central and South America, tropical parts of Asia, Australia, the Middle East and the Pacific islands4.Napier grass  source11. It has many other beneficial characteristics, including resistance to most pests and diseases, ease of establishment and fast regrowth capacity, withstanding repeated cuttings. It is also resistant to high temperatures, and has low water and nutrient requirements12. However, its productivity is primarily checked by the unpredictable weather arising from changes in climatic conditions14 and biotic stressors, such as the diseases head smut and Napier stunt, particularly in Central and Eastern African countries17.Napier grass is mainly cultivated as a forage crop for animal feed due to its high palatability and feed quality, high dry matter production (up to 78 tons of dry matter/ha/year), and year-round availability under supplementary irrigation18. Napier grass is a cross-pollinating allotetraploid species with a chromosome number of 2n = 4x = 28 (genome A’A’BB)20. The Napier grass A’A’ genome has been reported to be homologous to the AA genome of pearl millet 21. The two species are cross-compatible, producing triploid (AA’B genome) sterile hybrids that can only be propagated by vegetative means23. The hybrids are favored as a forage as they combine the superior forage quality of pearl millet with the high yielding ability of Napier grass1. Consequently, these interspecific triploid hybrids have become a crucial part of the improved forage crop value chain in Africa, Asia and South America4.The availability of genetic diversity is a prerequisite for breeding and genetic improvement programs. It serves to identify novel alleles for agronomically important traits, such as disease and pest resistance, drought tolerance, and withstanding challenges associated with the changing climatehttps://www.genesys-pgr.org) have collections containing a considerable amount of germplasm. Although Napier grass exhibits high genetic variation due to its strict out crossing and self-incompatibility nature, the global diversity is low due to its vegetative propagation and the common use of cuttings as planting material. This would likely have limited the exchange of genes between genotypes. However, limited information exists about the overall available genetic diversity and germplasm exchange among the global collections.Several national and international genebanks have conserved a substantial number of Napier grass genotypes collected from various geographical regions. For example, the International Livestock Research Institute (ILRI); the Brazilian Agricultural Research Corporation, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), United States Department of Agriculture (USDA-ARS), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Millennium Seed Bank Kew  , and other genotypes with unknown or different names, were included. We also included the 60 ILRI and 47 EMBRAPA genotypes, which have been studied previously32 to assess and visualize the overall diversity and identify new and unique genotypes. Out of these initially selected 581 Napier grass genotypes, genotypes with high missing values for the genome wide SNP markers were removed and a final set of 574 , 86 from EMBRAPA, and 23 from the USDA-ARS collections. The genotypes from the USDA-ARS were selected, based on a previous genetic diversity study on the collection19. The highest number of markers mapped onto chromosome CpA01 (8057 SNPs and 2947 SilicoDArTs) while the lowest number mapped onto CpB07 (4411 SNPs and 1245 SilicoDArTs).Genotyping generated 114,881 SNP and 46,293 SilicoDArT markers, distributed over the Napier grass genome. Approximately 86%  of the SNP and 66%  of the SilicoDArT markers were aligned onto the fourteen chromosomes of the Napier grass genome and about 2% (2653 SNPs and 918 SilicoDArTs) of the markers were mapped onto different contigsThe percentages of the markers missing values ranged from 0 to 22% for the SilicoDArT markers and 0 to 62% for the SNP markers. Heterozygosity (He) values for the SNP markers ranged from 0 to 0.5 with an average value of 0.15 while polymorphic information content (PIC) values ranged from 0 to 0.38 with an average value of 0.13 , missing values (≤ 10%), independence from each other (linkage disequilibrium-LD ≤ 0.5), and their distribution across the Napier grass genome. The PIC and He values of the selected markers ranged from 0.08 to 0.38 and 0.09 to 0.50 with an average value of 0.26 and 0.32, respectively, and about 60% of the markers had PIC and He values above 0.25  to 0.47 (between sub-clusters III and VI) in the eleven sub-clusters (Table P < 0.01) genetic variation among and within the major as well as the sub-clusters (Supplementary Table The pairwise 33 and fixation index (FST)34. The pairwise Nei’s genetic distance ranged from 0.018, between ILRI and KALRO/EMBRAPA, to 0.05, between the KALRO collection and progeny plants. Similarly, the estimated FST values ranged from 0.029, between ILRI and ICRISAT collections, to 0.101, between the KALRO collection and progeny plants (Table P < 0.001) both between and within collections, however, higher variation was detected within (90%) than between (10%) collections  and progeny plants were compared using Nei’s genetic distancePotential duplicate genotypes in each collection were identified using Nei’s genetic distance at a threshold of 0.025, which was equivalent to the identity-by-descent (IBD) kinship threshold (k ≥ 0.45) for self, or duplicate identification. A greater number of potential duplicates was detected in the KALRO collection, while all genotypes from ICRISAT were distinct Fig. . From th35 and Mendelian-inconsistent errors37 analyses. First, we examined the genetic relationship between the recorded seed parents and progeny plants by the IBD method of the maximum likelihood (ML) relatedness estimator35, and these were corrected based on the IBD result whenever there was a discrepancy between the recorded seed parent and the IBD result.We estimated the parentage of the 219 progeny plants, raised from 13 open pollinated seed-setting ILRI genotypes, using identity-by-descent (IBD)37. The Mendelian error counts ranged from 2 to 316 for PC1 , 1 to 401 for PC2 , and 37 to 937 for PPC , respectively. In general, we identified or confirmed seed parents, potential pollen parents, and possible crosses as complete parents for 189 (86%) of the progeny plants , using quality filtered DArTseq markers  and field phenotype data measured on 85 Napier grass genotypes, identified seven markers (6 SNPs and 1 SilicoDArT) significantly associated with fresh biomass yield. The associated markers mapped on chromosomes CpA01, CpA02, CpA03, CpA07, CpB01, CpB02, and CpB06 and explained 0.21–21% of phenotypic variation , Africa (mainly from Kenya), and Tifton (genotypes derived from the cultivar “Merkeron” in a breeding program)42.It is thanks to the diversity of plant genetic resources serving as a source of new alleles which support the development of new cultivars with desirable qualities, including breeder’ and farmers’ preferred traits such as high biomass yield, high nutritional quality, larger seeds, and pest and disease resistance43, an efficient and cost-effective sequencing technology that concurrently generates dominant SilicoDArT and co-dominant SNP markers (https://www.diversityarrays.com/services/dartseq/) with a high density and genome coverage. Similar to our previous reports44, we obtained a large number of markers, 114,881 SNPs and 46,293 SilicoDArTs, distributed over the fourteen chromosomes of the Napier grass genome. More than 80% of the markers (from both marker types) were able to be mapped onto the genome, approximately equally onto the A' and B sub genomes19. We applied strong SNP filtering criteria by setting a maximum of 10% missing values, a minimum of 5% minor allele frequency, a maximum of 0.5 linkage-disequilibrium (LD) value to ensure independent markers and, markers that were able to be mapped across the genome. The filtered polymorphic markers, with average PIC and He values of 0.26 and 0.32, respectively, were used in the genetic diversity analysis.We employed the GBS method of the DArTseq technology33. These genetic distance values were greater than the range that Wanjala et al.45 reported for 281 Napier grass genotypes. The high genetic variability detected could be attributed to the high levels of heterozygosity that exists in Napier grass due to its self-incompatibility and obligate outcrossing nature26. Napier grass is a highly heterozygous tetraploid species with a broad parental diversity and rich gene pool40.The Napier grass genotypes from the worldwide collections and progeny plants examined in this study demonstrated substantial genetic variation with an average value of 0.23 (0 to 0.41 range) Nei’s genetic diversity coefficientsFST values ranged from 0.14 to 0.15 between the three clusters and 0.17 to 0.47 between the 11 sub-clusters. This substantial genetic variation was detected both within and between clusters, while within-cluster variation was slightly larger (54%) than between-cluster variation (46%). Generally, outcrossing species show higher levels of genetic variation among genotypes and less population divergence, compared to self-pollinating species46. Similar results were reported in other outcrossing species such as Rhodes grass (Chloris gayana)47 and perennial ryegrass (Lolium perenne)48. Nevertheless, about 46% of the genetic variation was detected between the eleven sub-clusters. This higher divergence could be explained by the clonal propagation of Napier grass through stem cuttings, which reduces genetic drift and gene flow among genotypes within a population49. Genotype adaptation to specific environmental conditions might have also contributed to the high genetic diversity among the clusters, given that the genotypes were collected from different parts of the world.Furthermore, we detected three major clusters and eleven sub-clusters that were significantly different from each other, based on the admixture model in STRUCTURE, hierarchical clustering, PCA, and AMOVA analyses. The FST value of about 0.47, cluster III  and cluster VI (mainly from the ILRI and KALRO collections) had the highest genetic distance, followed by between cluster I  and cluster VI, and cluster III and IX . The results indicated a divergence between the collections and progenies, showing the potential of the progeny plants, that were produced from open pollination, for enhancement of genetic diversity in Napier grass collections and potential for future use in breeding programs, such as heterosis breeding. Heterotic contributions to increased biomass production in the F1 hybrids of Napier grass have previously been reported51.With an 18. The genotypes in cluster II are low to medium biomass yielders, whereas the genotypes in clusters VI and VII are medium to high biomass yielders5. From the EMBRAPA collection, the majority of the elite lines , which have been identified for their superior biomass yields, drought tolerance, and comparatively higher protein content5.The ILRI genotypes were distributed across sub-clusters, mainly in sub-clusters II, VI, VII, IX, and the admixture groups, and their clustering was mostly consistent with our previous report28, genotypes derived from the cultivar ‘Merkeron’ were clustered together in this group. ‘Merkeron’ is a well-known cultivar in the USA, released by a breeding program in Tifton, Georgia50 and was derived from an intraspecific cross between a high yielding clone and a dwarf leafy clone, with improved yield and disease resistance29. Among these, the well-known variety “Mott” (Tift N75), a dwarf leafy type selected from progeny of selfed Merkeron30, is included in this cluster. The variety "Mott" was introduced to ILRI, has been preserved in the genebank with accession number ILRI_15743 and distributed to farmers and national research institutes in Ethiopia. However, none of the samples collected from farmers’ fields and national research institutes, as well as the genotype labeled as ILRI_15743, clustered in this group. Similar findings were reported by Harris et al.28, who found that none of the "Merkeron" samples taken from the various countries where "Merkeron" was distributed were genetically identical, which possibly happened due to genetic mixing up through outcrossing.Cluster XI was primarily comprised of USDA-ARS genotypes, with a few EMBRAPA genotypes. In line with the report by Harris et al.C. purpureus × C. americanus hybrids including the commercial varieties ‘Maralfalfa’53, ‘Super-Napier’, and the hybrids from the ILRI collection ILRI_16840, ILRI_16837, ILRI_1535718. ILRI_15357 showed very high genetic similarity to ‘Maralfalfa’, with more than 0.47 IBD value. Maralfalfa has been reported for its adaptability to higher altitudes (3000 m asl) and for high biomass yield (60 tn/ha) with higher protein content53. Many genotypes from Kiambu under the KALRO collection were also found in this cluster, perhaps suggesting that there are also hybrids held in this collection.Cluster VII consisted of most FST values of 0.20 to 0.33 and an average value of 0.25. The progenies in cluster I and III were particularly distinct from the other clusters with an average FST value of 0.32, showing their divergence from genotypes and other progenies. Napier grass's high heterozygosity within genotypes, self-incompatibility, and obligate outcrossing nature54, which can increase the likelihood of developing new genetic make-up through recombination, may be responsible for this high level of diversity and population stratification in progeny plants.The progeny plants were almost exclusively distributed in clusters I, III, IV, and X, which were significantly different from each other with a range of FST range of 0.024 between the ILRI and USDA-ARS collections to 0.072 between the KALRO and ICRISAT collections. The genetic variation between collections explained only about 10% of the variation as compared to 90% of the variation explained by genotypes within collections, indicating a degree of overlap and redundancy that may have resulted from sharing and exchanging materials among the genebanks. Although Napier grass genotypes are primarily maintained via vegetative multiplication using cuttings, there is still some degree of hybridization existing in nature and also controlled crossings in some collections26. Such redundancy of genotypes between genebanks could also have arisen because each genebank or breeding program gives its own different accession numbers, as there is no standardized labelling and registration procedure. Facilitated by the International Treaty on Plant Genetic Resources for Food and Agriculture, the ILRI forage genebank has assigned a digital object identifier (DOI) to each genotype held in its genebank47, which could be a promising solution to this issue but should then be adopted by more national and international genebanks and research institutions.The collections we studied generally exhibited low to moderate genetic differentiation with an 32.The presence of genotype overlaps and redundancy among the collections was further confirmed by Nei’s genetic distance and identity-by-descent (IBD) analysis; the largest overlap was between the ILRI and KALRO collections, followed by between the KALRO and EMBRAPA, and the ILRI and EMBRAPA collections. The substantial overlap between the ILRI and KALRO collections could be due to the close proximity of the two institutes and historical sharing of genotypes, as indicated in the genotypes name, a large number of KALRO's genotypes were sourced from the ILRI collection. This suggests that the introduction and incorporation of new genotypes to a collection should be carried out strategically to minimize duplications and capture only new genetic diversity that is distinct from the existing one, as conservation is a costly businessWithin collection genotype redundancy analysis, also showed a greater number of potential duplicates in the KALRO collection whereas all genotypes from ICRISAT were distinct. While genotype redundancies and overlaps between collections from various institutions, especially those from different continents like Africa, South America, Asia, and North America, can be taken into consideration and should be encouraged to preserve genotypes with specific adaptations to a wide range of farming systems and agroecological conditions, overlap within a collection should be reduced as it imposes a conservation cost. However, it is crucial to carefully remove overlaps using molecular marker analysis supported by field phenotype and passport data whenever possible in order to preserve unique alleles in closely related genotypes.FST values ranging from 0.067 between the progenies and ILRI collection to 0.101 between the progenies and KALRO collection. This finding further supports the potential of progeny plants to increase genetic diversity with the potential to be used in breeding programs in the future. Open-pollinated progeny selection has been one of the breeding approaches in Napier grass and has resulted in the generation of improved cultivars1. Progeny plants from an open-pollinated population of cultivars ‘A146’, ‘A148’ and ‘A149’ were developed in Taiwan, and the progenies produced 20% more dry matter than the seed parents, had very few hairs on the leaves, flowered late and grew more erect55. However, the use of open pollination breeding schemes allows the control of only one-half of the genome of the resulting progeny material because contributions from pollen parents are not controlled, and the transfer of undesirable traits from unimproved lines can limit or slow the successful release of new cultivars. Therefore, Napier grass breeding programs should focus on targeted crossings that have the potential to yield higher rates of success50 in rapidly generating desirable genotypes and eventually culminating in hybrid breeding wherein a superior individual can be maintained by vegetative propagation after being identified.When compared to the variation among the five collections, the progeny plants were genetically separated, with 37. The progenies in clusters I and III were produced primarily from the ILRI_P9_16821 and ILRI_P6_16835 genomes, while the progenies in clusters IV and X were dominantly from ILRI_P7_16837 and ILRI_P13_16790 and ILRI_P1_1026 and ILRI_P8_16803 genomes, respectively. These results could offer a stronger basis for selecting superior progeny plants for further selection and improvement through heterosis breeding in Napier grass.In this study, we were able to identify potential seed and pollen parents for more than 80% of the progeny plants using identity-by-descent (IBD) analysis based on the maximum likelihood (ML) relatedness estimator and Mendelian-inconsistent errors44.A genome-wide association study identified seven QTL regions associated with forage biomass yield, which represents the above-ground fresh biomass production. The QTLs on chromosome CpB06 and CpA07 showed a strong association and were detected by five different statistical models, suggesting that they can potentially be exploited in marker-assisted selection to develop high biomass producing Napier grass varieties. A marker on chromosome CpB06 that was strongly associated with fresh and dry biomass yields and water use efficiency has previously been reportedThis study represents the first comprehensive genetic diversity study in global Napier grass collections and revealed the presence of extensive genetic variation among Napier grass genotypes. The analysis detected major clusters and sub-clusters that were significantly different from each other with higher within cluster/sub-cluster genetic variation. The study provides a global picture of the available genetic diversity of this important tropical perennial crop and the findings could be used to guide targeted use and conservation of the diversity by breeders and genebanks. Additionally, the information can be used to determine heterotic groups for future Napier grass breeding and improvement through heterosis breeding. Given that the genotypes were collected from different parts of the world, which shows their potential adaptation to a wide range of environmental conditions, studies on genotype by environment interactions are necessary in the future.The genetic variation amongst the different genebank collections studied here was generally low and various levels of genotype redundancy were detected both within and across collections. Genotype overlaps between genebanks, particularly those from different continents, should be encouraged in order to preserve genotypes with specific adaptations to a wide range of agroecological conditions. To reduce the cost of conservation, overlaps within a genebank should be minimized. However, overlaps within a genebank must be carefully removed to maintain unique alleles in closely related genotypes.Higher genetic variation and divergence was detected in the progeny plants produced from open pollination, which creates new genetic makeup and diversity. Incorporating some of the distinctive progeny plants into the genebank collections will be advantageous to enhance the genetic diversity and population size of the collections. However, a few genotypes predominated as a pollen source, as suggested by the parentage analysis, which invites further research on the mechanisms of pollination in Napier grass genotypes maintained in the ILRI forage genebank.53 and Super-Napier (Pakchong 1)—both reported as hybrids of Napier grass and pearl millet with high biomass yield and protein content, were included. An additional 19 genotypes, collected from different places, with the name ‘Mott” or with unknown names were also included , Nairobi, Kenya for genotyping.Young leaf samples from each individual plant were collected into 2 ml Eppendorf tubes, stored in an icebox containing frozen ice, and transferred to a − 80 freezer as quickly as possible. The samples were freeze-dried for about 48 h and ground into fine powder using a tissue lyser. Genomic DNA was extracted from the leaf powder using a DNeasy® Plant Mini Kit (250)  following the manufacturer’s procedures. The concentration and quality of the extracted DNA was assessed using a Nano-drop spectrometer (DeNovix DS-11 FX spectrophotometer) and 0.8% agarose gel electrophoresis. The DNA concentration was adjusted to 50 to 100 ng/μl in 96 well semi-skirted plates and sent to SEQART-AFRICA  markers, combining the DArT complexity reduction with sequencing on the Next Generation Sequencing (NGS) platforms43. The markers were aligned with the Napier grass genome19 using the short sequence fragments associated with each marker to generate information on map position of the sequences and markers across the genome. A heatmap showing the density and distribution of markers across the fourteen Napier grass chromosomes was generated using a free online tool at https://www.bioinformatics.com.cn/en. For genetic diversity analysis, polymorphic markers were filtered based on the marker’s minor allele frequency (MAF), missing values, and independence from each other  using the dartR56 and SNPRelate57 R-packages in R statistical software .The genotyping was conducted using the GBS (Genotyping By Sequencing) method of the DArTseq platform58 using the filtered polymorphic markers. The burn-in time and number of iterations were both set to 100,000 with 10 repetitions, assuming hypothetical subpopulations (K) ranging from 1 to 20 in an admixture model with correlated allele frequencies. The results of the run were uploaded to the software “Structure Harvester” (http://taylor0.biology.ucla.edu/structureHarvester/) and the most likely number of subpopulations was determined by the Evanno method59. Genotypes with less than 60% membership probability were considered admixed. Population structure was further examined using hierarchical clustering and principal component analysis (PCA) using Poppr60 and adegenet61 R-packages. Genetic diversity was estimated using pairwise Nei’s33 genetic distance.Population structure and sub clustering among the Napier grass genotypes was assessed based on the Bayesian algorithm implemented in STRUCTURE (ver. 2.3.4) software62 and fixation index (FST)34 in the R-packages Poppr60, adegenet61, and APE63. AMOVA was used for partitioning the total variance in allele frequencies within and among clusters, sub-clusters, and collections and to assess the significance level of the variations. FST, which ranges from 0 to 1, assesses the degree of genetic differentiation between clusters, sub-clusters, and collections. A value of 0 indicates no differentiation while a value of 1 indicates complete differentiation. In general, an FST value of 0:00 to 0:0.05 indicates low differentiation, 0:05 to 0:0.15 indicates moderate differentiation, 0.15 to 0.25 high differentiation, while FST > 0.25 are considered to represent very high levels of differentiation64.Pairwise estimates of population differentiation and divergence between clusters, sub-clusters, and collections were analysed by Analysis of MOlecular VAriance (AMOVA)35 using the R package SNPRelate57 and by counting the Mendelian-inconsistent errors using a custom R script reported in Vanderzande et al.36. The IBD analysis was mainly used to confirm the genetic relationship between the recorded seed parents and progeny plants, whereas Mendelian-inconsistent errors count was used to determine any putative set of parent–child (PC) and parent-parent–child (PPC) relationships. In the Mendelian-inconsistent errors count, a low percentage of errors was used as an indication of putative parent–offspring relationships while higher percentages were indicative of more distant relationships65.Parentage and genetic relationship analyses were conducted using identity-by-descent (IBD) analysis based on the maximum likelihood (ML) relatedness estimatorA subset of 85 Napier grass genotypes were planted in the main rain season (mid-June to mid-September 2018) using an augmented p-rep design with two replications at Addis Ababa, Ethiopia. Six stem cuttings per genotype were planted in a single row with a distance of 750 mm. After establishment, the plants were clean cut every 12 weeks of regrowth to a standard height of 50 mm above ground. Total fresh biomass yield per plant (TFW) was taken by weighing and calculating the average from three randomly selected plants per row. The average value per genotype was calculated based on 24 plants, taken from three plants per row, in two replications, across four harvests from the 2018 to 2021 cropping seasons.66. The models used include General Linear Model (GLM), Mixed Linear Model (MLM), Multiple Loci Mixed Model (MLMM), Fixed and random model Circulating Probability Unification (FarmCPU) and Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). A total of 16,083 markers  were retained after filtering. The markers were filtered based on call rate (≥ 0.90), minor allele frequency (MAF ≥ 5%), missing values (NA < 10%), and their distribution across the genome.Marker-trait association analysis was conducted using five statistical models implemented in Genomic Association and Prediction Integrated Tool version 3 (GAPIT3)Supplementary Tables.


Understanding reasons for why people choose to have or not to have a genetic test is essential given the ever-increasing use of genetic technologies in everyday life. The present study explored the multiple drivers of people’s attitudes towards genetic testing. Using the International Genetic Literacy and Attitudes Survey (iGLAS), we collected data on: (1) willingness to undergo testing; (2) genetic literacy; (3) motivated cognition; and (4) demographic and cultural characteristics. The 37 variables were explored in the largest to-date sample of 4311 participants from diverse demographic and cultural backgrounds. The results showed that 82% of participants were willing to undergo genetic testing for improved treatment; and over 73%—for research. The 35 predictor variables together explained only a small proportion of variance: 7%—in the willingness to test for Treatment; and 6%—for Research. The strongest predictors of willingness to undergo genetic testing were genetic knowledge and deterministic beliefs. Concerns about data misuse and about finding out unwanted health-related information were weakly negatively associated with willingness to undergo genetic testing. We also found some differences in factors linked to attitudes towards genetic testing across the countries included in this study. Our study demonstrates that decision-making regarding genetic testing is influenced by a large number of potentially interacting factors. Further research into these factors may help consumers to make decisions regarding genetic testing that are right for their specific circumstances. Since the completion of the Human Genome Project in 2003 humanity has entered the Era of the Genome . This neWidespread availability of genetic testing may speed up the development of personalized medicine—optimized prophylactic or therapeutic solutions based on individuals’ genetic make-up , see e.gRelatedly, research has begun to examine people’s attitudes and views about genetic testing, indicating a trend from resistance to greater acceptance (see e.g. ). SeveraResearch has also indicated that willingness to test for medical purposes is higher for treatable conditions and conditions with a clear family transmission patterns , 21, 22,Understanding factors that shape attitudes towards genetic testing is an important agenda for the Genomic Era. Several studies found that people’s views on genetic testing are related to their genetic literacy , 29, 30.Genetic knowledge, however, does not explain much of the variance in people’s decision regarding genetic testing, as evidenced in the low correlation between them . This iMotivated cognitions relevant to genetic testing can also include beliefs regarding the malleability of different traits and an individual’s control over them. This is because such beliefs may influence one’s evaluations of usefulness of genetic information. For example, people’s beliefs regarding ‘free will’ have been linked to such phenomena as pursuit of self-directed goals, level of prosocial and aggressive behavior, autonomy and conformity, self-efficacy and perceived capacity . In publThe attitudes towards genetic testing may also depend on whether people believe that genes are involved in traits , data from 4311 participants were analyzed. As most of participants came from Russia, Nigeria, USA and UK . The current version of iGLAS is available in 9 languages . A short sample of the English language version of iGLAS can be found at found in . The latIn the present study we used 2 items to explore willingness to undergo genetic testing and 37 items—as potential predictors of the willingness to undergo genetic testing. The predictors represented three groups of potential sources of individual differences: 1) genetic literacy; 2) motivated cognition; and 3) demographic characteristics.Willingness to undergo genetic testing. Two questions explored participants’ willingness to undergo genetic testing, using 7-point Likert scales :Would you take a genetic test if it allowed you to have improved treatment ” (Henceforth Test for treatment);“Would you be willing to give a sample of your DNA for scientific research if your data are stored anonymously?” (Henceforth Test for science).“Genetic literacy was measured with 20 items.Which of the following 4 letter groups represent the base units of DNA: 1) GHPO; 2) HTPR; 3) GCTA; and 4) LFWE?”. More details about the genetic knowledge items can be found in [Genetic Knowledge score was obtained by summing up correct responses for each item.The questions were developed to assess a basic functional level of genetic knowledge. An example item with 4 response options being: “found in . In the Motivated cognition was assessed with 10 items.In deciding whether to take a genetic test, which of the considerations below apply to you” . 1 item tapped into views on potential data usage violations Mistrust in research: “I do not trust research institutions in my country because they might misuse data obtained from participants”. A 7-level Likert scale (from “Strongly disagree” to “Strongly agree”) was used for both questions.1 item tapped into views on genetic influences on behavior: “Destiny is written in our genes” , and level of Religiosity .The study was approved by the Goldsmiths Department of Psychology Ethics Committee (ref: PS101016RCS). All the analyses presented in this paper were based on data accessed after ethical approval for the study was provided by the ethics committee of the psychology department at Goldsmiths, University of London on 10/10/2016. Participants answered iGLAS items online, in their own time and place; or at their University. The data were fully anonymous. Informed consent was implemented at the beginning of the survey. Participants were asked to accept the following statements: My participation in this study is voluntary; I am over 18; I may withdraw from this research at any time and for any reason; I may omit any questions I do not wish to answer; All data will be treated with full confidentiality and, if published, it will not be identifiable as mine. For those who did not endorse any of the statements, the survey discontinued.https://personality-project.org/r). Significance level was set to p < 0.05. Mixed-effect modelling was performed with lme4 package [https://osf.io/nex76/The data were analyzed with R language for statistical programming and R studio platform . The regression and correlation (Pearson) analyses were performed with in-built R functions “lm” and “cor”, the factor analysis was performed with the ‘psych’ package .As can be seen from Test for treatment and 72.78%—for Test for science questions.We also computed percentages for each response option, showing that 81.64% of the respondents chose “Very likely”, “Likely”, and “Somewhat likely” for Zero-order correlations (Pearson) among all study variables are presented in Genetic knowledge (r = ~.20), as well as with Age (r = ~.10), Education level (r = ~.06) and deterministic views ; and negatively correlated with Religiosity (r = ~.11) and Mistrust in research (r = ~.08). Data security and Health issues concerns showed only negligible correlations with willingness to undergo testing. Educational level did not correlate with willingness to undergo Test for treatment and only negligibly with Test for science.Willingness to undergo genetic testing in both contexts was positively correlated with Test for treatment and Test for science. Genetic knowledge, Destiny is written, Religiosity, Mistrust in Research and Gender were positively associated with Test for treatment, together explaining approximately 7% of the variance. The same set of significant predictors were shown for willingness to undergo Test for science, together explaining 6.3% of the variance  but with a random intercept for Country of residence–random slope model did not fit because of small sample sizes for some countries (15 countries with N = 1). The mixed model analysis showed a result similar to stepwise regression (i.e. same predictors being significant), explaining 6.2% in Test for treatment with extra 0.3% when random effects were accounted for. For Test for science the mixed model analysis showed an almost similar result, with fixed effects of Genetic Knowledge, Destiny is written, Religiosity, Mistrust in research and Gender being significant. The only extra predictor was Data security . This set of predictors explained 7.1% in Test for treatment with no extra addition from random effects. Extra details on the analysis are available from authors on request.To control for effect of We next examined potential cross-cultural differences, re-running the analyses for four countries with the biggest samples separately: participants from Russia (N = 1358), Nigeria (N = 1020), the USA (N = 340) and the UK (N = 295). Test for treatment, the highest endorsement of testing  was shown for UK (87.51%) and USA (86.46%), followed by Russia (78.69%) and Nigeria (74.11%). The pattern was the same for the overall endorsement of Test for science, with the highest endorsement in the UK (82.42%) and the USA (81.18%), followed by Russia (67.36%) and Nigeria (67.48%).Our data showed that frequencies for willingness to undergo testing were quite different across the 4 countries, with small effect. For Age, UK and USA participants were considerably older than those from Nigeria and Russia; the overall Genetic knowledge was higher in UK and USA samples, compared to those from Nigeria and Russia; and Religiosity was higher in Nigeria than in the other 3 countries. There were some differences across the 4 countries in the Educational level, with Educational level for Russia being lower when in Nigeria, USA and UK; and no differences across the three : level of Genetic knowledge and the deterministic views (Destiny is written). For other predictors, there were differences across the countries. For example, greater Mistrust in research institutions and Health issues  were moderately associated with less willingness to undergo genetic testing for treatment (Test for treatment) only in the UK sample. More concerns regarding Data security were associated with less willingness to undertake Test for treatment in the USA sample, and, surprisingly, with more willingness in the Nigerian sample. Greater religiosity was associated with less willingness to undergo Test for treatment only in the Russian sample. Overall, more variance was explained by the predictors in the UK and the USA (17 and 13%), than in Nigeria and Russia (~6%).None of the predictors of Test for science, the only significant predictor for all countries was Genetic Knowledge. Deterministic views (Destiny is written) was positively linked to Test for science in three countries (except the UK). Mistrust in research was negatively linked to Test for science in the three countries (except Nigeria). The pattern of results was somewhat different for other predictors. For example, Data security was negatively associated with Test for science only in the USA sample. Health concerns were negatively associated with willingness to Test for science in the UK; and positively–in Russia. Religiosity was negatively associated with Test for science only in the Russian sample. Overall, more variance was explained by the predictors in the USA and the UK (13 and 14%), than in Nigeria and Russia (2 and 5%).For the The current study explored attitudes towards genetic testing and the sources of individual differences in such attitudes in the largest to-date sample of participants from diverse demographic and cultural backgrounds.Our data showed that 82% of the 4311 participants were willing to undergo genetic testing for improved treatment; and 73%—for research. This finding is in line with previously shown positive attitudes towards genetic testing in samples from different countries , 70–72. Analysis of willingness to undergo testing in separate countries with the largest samples in the current study  showed lower willingness in Russia and Nigeria. Several sample characteristics could have contributed to the observed differences. For example, USA and UK samples were on average older  and had higher genetic literacy (.33). In addition, USA and UK have higher GDP , which mThis study also explored factors that might explain individual differences in people’s willingness to undergo genetic testing in the overall sample and within the four countries. We examined genetic knowledge, motivated cognition (beliefs and concerns), and demographic characteristics—as potential predictors. Overall, the investigated factors explained little variance in willingness to undergo genetic testing: around 7% in testing for treatment and 6% for research. The effects of individual predictors were weak, with genetic knowledge being the strongest positive predictor for both treatment and research. Contribution of genetic knowledge to willingness to undergo testing is expected and in line with previous research , 18. OurOn the other hand, genetic knowledge explained only a little variance in willingness to undergo genetic testing, which indicates the complex nature of this association. Some research suggested that greater genetic literacy is associated with greater reported fear and uncertainty about the implications of genetic testing; and, in contrast, lack of understanding and unreasonable expectations may lead to unwarranted enthusiasm about testing and unrealistic hopes about its results , 78. OurIn line with our prediction, the belief that ’one’s destiny is written in one’s genes’ was positively associated with willingness to undergo genetic testing (.08-.12). Given that genetic testing can provide probabilistic prediction of future life outcomes , it seeFurther analysis of predictors for willingness to undergo genetic testing in UK, USA, Nigeria and Russia separately showed both similarities and differences across the four countries. Genetic knowledge and belief in ‘genetic destiny’ predicted willingness to test for treatment in 3 of the 4 countries. The absence of these effects in the UK can be explained by specific characteristics of the UK sample in this study. For example, this sample was the oldest out of the four (Mage = 43.73) and had the highest average education level (4.37). However, the overall effect of Level of education was small . Moreover, the UK participants showed the highest overall willingness to undergo test for treatment and the narrowest distribution of scores around the mean. This restricted variance may have led to the observed lack of prediction.Several predictors that were not present in the overall sample emerged in the analysis of data for separate countries. For Test for treatment, data security was a significant predictor in Nigeria and the USA; while health-related concerns—in Russia and the UK. One of the strongest predictors in the UK sample was mistrust of research institutions (-.33), which is in line with some previous studies . Such miData security concern was a significant negative predictor of willingness to test for science only in the USA. This negative effect on a decision to undergo genetic testing is expected, as recently there have been many public outcries regarding data protection and storage, including public cases of companies selling genomic data for profit. For example, 23andMe sold the data of participants to GlaxoSmithKline for $USD300 million . Given tContrary to the pattern obtained for the whole sample, health-related concerns were a significant negative predictor in the UK; and a significant positive predictor in Russia for testing for treatment and science. A negative effect of health-related concerns is in line with previous studies that showed people worrying about gene-based health information. For example, US citizens were found to be afraid that genetic test results could be used to discriminate against those with a genetic predisposition for illness ; or thatI would rather not know of any potential debilitating diseases in my future’.Moreover, in the Russian sample the effect was weak and positive, suggesting that people with more concerns were actually more likely to undergo testing for research. Though small, this effect might be reflective of a general willingness to obtain some valid medical information regarding one’s health-related concerns irrespective of the source of such knowledge. This result again stresses the need for improvement of health-related literacy in the general population, including through genetic counselling. In the Era of Genome genetic counselling should be more readily available as it allows to support and educate people about medical, psychological and familial aspects of heritable diseases . For exaThe current study has a number of limitations. Firstly, the examined predictors explained only a small proportion of the overall variance in willingness to undergo genetic testing. This means that further research must explore additional factors and potential interactions among them. Secondly, some effects varied as a function of the sample. These differences across countries are likely to result from multiple factors that were beyond the current research, including differences in educational systems, socio-economic status, and media coverage of genetic-related questions (e.g. novel findings or regulations introduced). Some of these differences might stem from differences in recruitment strategies . Further, Nigerian and Russian sample were on average younger compared to UK and USA. However, the effect of age on willingness was very small and was not significant in a step-wise regression. Thirdly, the current study was correlational and did not allow causal inferences. Fourthly, only 4 out of the 86 countries were represented by substantial numbers of participants. As such, a truly cross-cultural study was beyond the scope of this paper. Another limitation might be posed by lengthy data collection, as it might introduce some uncontrolled confounds (e.g. media-related changes in attitudes or effects of Covid-19 pandemic). However, data in different countries were collected in parallel and most collection was made prior to the pandemic. Finally, the current study did not investigate willingness to undergo genetic testing beyond medical and research contexts, such as for obtaining information on non-medical traits, kinship and ancestry , and criGenetic testing is at the forefront of discussions about healthcare, health insurance and disease prevention. Thus, it is important to investigate factors affecting decision-making regarding the willingness to undergo genetic testing. Our data showed positive attitudes towards genetic testing in the full sample and in separate countries. Genetic literacy, data protection and general trust in research institutions were among the robust predictors of willingness to undergo genetic testing. However, despite the large number of variables explored in the study, only a small proportion of variance in willingness to undergo genetic testing was explained. This suggests that decision making in this field is a product of many, potentially interacting, factors. Better understanding of the reasons for decisions regarding testing would help people to make decisions that are right for their specific circumstances.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Vallisneria natans in a subtropical lake. Results showed that this species maintained a relatively high genetic diversity  and epigenetic variation . Water body temperature and chlorophyll a density were positively correlated to the genetic and epigenetic variations. The clonal generates of V. natans depicted a relative high methylation level and shew ancestral scenario between the genet and the second clonal generation. These findings revealed that species diversity is unevenly distributed under environmental heterogeneity, even at a fine geographic scale. Environmental characteristics in relation to temperature and chlorophyll a should be considered in the analysis of the genetic and epigenetic variations. Additionally, epigenetic variations between genets and ramets should be considered with caution when applied to analysis of other aquatic species.Plant genetic diversity differs in habitat's oscillations, especially species distributed under heterogeneous environmental conditions. Freshwater ecosystems are vulnerable to biotic and abiotic impacts, which affect the genetic and epigenetic variations in aquatic plants. The extent of environmental heterogeneous attributes can be examined based on genetic and epigenetic variations. Such variations under environmental gradient can provide evidence for understanding the correlations between rapid environmental changes and species evolution. In this study, we performed amplified fragment polymorphism length and methylated‐sensitive amplified polymorphism analysis to depict the genetic and epigenetic variations of  Vallisineria natans in Liangzi Lake and found temperature and chlorophyll density were correlated to the species' genetic diversity and epigenetic variation.Then, based on the associated environmental factors, we constructed the priority restoration areas.We analyzed the genetic diversity and epigenetic variation of  The integrity of the genomic DNA was checked using 1% agarose gel electrophoresis against a molecular weight standard. DNA concentrations were determined using Nanodrop Spectrophotometer (Thermo Fisher Scientific).Our study focused e Figure , Table 12.2V. natans. The detailed procedures for AFLP and MSAP analyses and statistical analysis are described in Appendix Amplified fragment polymorphism length and methylated‐sensitive amplified polymorphism markers were used to evaluate the genetic and epigenetic diversity of An initial selective PCR analysis of eight individuals across four populations was carried out with 12 primer combinations; only the primers that provided clear, reproducible bands with sufficient polymorphic variation between populations were used in the analysis. The eight most informative primer combinations  were retained for AFLP analysis. Five primer pairs  were retained for MSAP analysis. To estimate the error rate in AFLP and MSAP genotyping, we randomly select 12 DNA samples to duplicate the AFLP and MSAP procedure.2.3Chl‐a) were measured using the method recommended by the National Groundwater Analysis Standard Procedure (GB 3838‐2002). The concentrations of six heavy metal elements  in the water samples were analyzed by inductively coupled plasma‐mass spectrometry . Distance to the nearest village (DTV), distance to the road (DTR), and number of village populations (NHV) were acquired using QGIS v3.28 (http://www.qgis.org) and through field survey by locals. The global climate and weather database from Worldclim v2.1 (http://worldclim.org) was used to extract 19 bio‐climates attributes at a 30‐arc‐s resolution. Previous studies indicated that multicollinearity in variables might lead to the overfitting of the models, temperature (Bio1‐11) and precipitation (Bio12‐19) were divided into two groups, and Pearson correlation coefficients were calculated to reduce the multicollinearity. One variable in each pair with Pearson's correlation coefficients (r) > .8 was eliminated. As topographic slope strongly influences the local temperature and the thermal conditions of the habitat, the slope aspect of each sampling site was analyzed based on the attributes of the topography of Liangzi Lake from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global digital elevation.During the assessment of habitat heterogeneity, 41 environmental variables were analyzed Table . Elevati2.4EcoRI/MspI and EcoRI/HpaII (1/1) indicates unmethylated conditions; fragments present only in EcoRI/MspI profiles (0/1) denote cytosine hemi‐methylation; fragments present only in EcoRI/HpaII (1/0) are considered as hemi‐methylated CHG sites; and absence of fragments of both profiles (0/0) representing uninformative states caused either by different methylation types or restriction locus polymorphism. To separate the unmethylated and methylated fragments, we divided loci as either methylated‐susceptible loci (MSL) or non‐methylated loci (NMSL) and a 0/1 binary matrix was also constructed where “0” represents a non‐methylated locus and “1” represents a methylated locus.Amplified fragment polymorphism length and MSAP genotypes were obtained from ABI sequencer 3730XL (Thermo Fisher Scientific). Fragments between 100 and 500 bp were detected using GeneMarker v2.2.2 software , (2) mean Shannon's information index (I and eI), and (3) percentage of polymorphic loci (PPL and ePPL).Indices of genetic diversity and epigenetic variations within populations were obtained from POPGENE v1.3.2 Yeh,  assumingCT), among populations within groups (øSC), and within populations (øST) were examined by analysis of molecular variance (AMOVA) using the R package poppr  dendrogram was constructed to divide the genetic groups among populations with the R package ape  was set to vary from 1 to 9, with each run having 5 × 106 Markov chain Monte Carlo (MCMC) iterations following a burn‐in period of 106 steps. The likelihood values were calculated to partition the populations into groups based on different K values with STRUCTURE HARVESTER v0.6.94  and epigenetic indices . Bray–Curtis distance was calculated among the samples. The Euclidean geographic distance and Euclidean environmental distance were also computed using the coordinates of sampling sites and environmental variables, respectively. Mantel test was performed using the R package vegan  using R package Boruta  after FDR correction.We used Bvstep method in R package sinkr Taylor,  with a tDistance‐based redundancy analysis (db‐RDA) was performed using the vegan package to quantify the pure and combined effects of environmental variables on genetic and epigenetic variations. This multivariate ordination technique is used to test whether the variations in one independent variable can explain the variance of another independent variable. Furthermore, the contributions of environmental variables were determined by variance partitioning analysis (VPA) and hierarchical partitioning (HP) using the multiple regression algorithm in the R package rdacca.hp  and detected the correlations between environmental variables and genotypic variations in R package lfmm , which can determine complex multivariate relationships among variables, to quantify the direct and indirect effects of environmental variables on genetic and epigenetic variations. The PLS‐PM does not require any distributional assumptions about the data, which are usually difficult to meet in natural ecosystems. We established a model based on the expected relationships and key drivers among environmental variables, genetic variation, and epigenetic variation with the R package plspm  clustering method in R package kohonon , mean Shannon's information index (I), and mean percentage of polymorphic loci was 0.320%, 0.474%, and 85.93%, respectively. At the population level, the percentage of polymorphic bands (PPL) of each population varied from 79.93% (V9) to 92.02% (V5). Population V5  exhibited the highest level of genetic diversity, and population V9  showed the lowest level of genetic diversity  and population V7  showed the lowest level of epigenetic variation  were methylated loci. The percentage of methylation polymorphism among populations varied from 76.18% (V3) to 90.60% (V5 and V8), with an average of 83.91%. Population V5 demonstrated the highest levels of epigenetic diversity  were classified as cluster 1, whereas populations from higher water body temperature and Chl‐a concentration areas  were classified as cluster 2.In the population structure analysis, the highest likelihood at t Figure . Clades 3.4r = .52, p = .0013)   and epigenetic variation  indices presented an obvious correlation with WT and Chl‐a .The Mantel test revealed a significant pattern of correlation between genetic and epigenetic variations with environmental variables  Figure . We filt) Figure b. In com) Figure a, 19 env) Figure . ParticuChl‐a, locus245 was associated with TDS and Sal  Figure , indicatl Figure .Chl‐a presented a vital effect and had the largest contribution to genetic diversity and epigenetic variation . In fact, WT, Chl‐a shared the effects on genetic diversity (combined effect = 29.46%) through variance partitioning analysis, WT exhibits the highest individual effect on epigenetic variation through hierarchical partitioning  , the first two axes explained 99.37% of the variances, with db‐RDA axes 1 and 2 accounted for 93.03% and 6.32% of the genetic and epigenetic variation, respectively Figure . The WT ) Figure .R2 = .658) of variation in genetic diversity  and epigenetic variation  . However, water quality had positive effects on genetic diversity  and epigenetic variations . A strong impact was detected from genetic diversity to epigenetic variation .Our PLS‐PM model showed the robustness of our data, as suggested by the goodness of fit (GoF = 0.91). The PLS‐PM analysis determined that WT had a negative influence on variation, accounting for 65.8% . However, in eight V. natans genets and second clonal individuals, although the first clonal generations variated from genets (MI = 1.081), the second clonal generations depicted lower epigenetic variation rates (MI = 1.020) than the first clonal generations. These finds indicated an ancestral scenario.The SOM cluster showed the changing loci from the genets to ramets with varied methylated rates Figure . Based oAmong the five comparison values in the analysis, aHash, dHash, Hist with split, and Single Hist were consistent at the methylated locus level between genets and the second clonal generations Table . The valFrom the aspect of different methylation types, the 22 ramets did not show devastating variations, with 80.0% identity of NMSL, 90.0% of HEML, 100% of INCML, and 97.3% of FML Figure a. Howeve3.6Chl‐a, genetic variation, and epigenetic variations, the restoration patterns are constructed based not only on the levels of genetic and epigenetic variation but also on environmental variables. Therefore, both WT and Chl‐a were used as constraint factors during the construction of restoration patches.The lake area was divided into priority and overall important areas for species restoration based on genetic diversity and epigenetic variation Figure . Due to 44.1V. natans populations under WT and Chl‐a gradient , consistent with a previous genetic study using ISSR markers of the same genera (52%–62% PPL) , high genetic and epigenetic variations were found to be correlated with low temperatures. By using the published data, we detected negative correlations between annual mean temperature and the genetic diversity in Hydrocotyle vulgaris , of which temperature promotes the shaping of the genetic structure.One of the questions raised by our study is whether the strong genetic structure is driven by geographic isolation or environmental factors. The answer is that environment factors may contribute to the occurrence status of genetic structure of 4.3Multicellular organisms are genetic mosaics owing to their somatic mutations. Somatic genetic variation may play a different, more positive role in multicellular species undergoing clonal reproduction ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); resources (lead); software (lead); visualization (lead); writing – original draft (lead); writing – review and editing (supporting). Xuyao Zhao: Formal analysis (supporting); resources (supporting); writing – review and editing (supporting). Manli Xia: Investigation (supporting); methodology (supporting). Hongwei Hou: Conceptualization (lead); funding acquisition (lead); project administration (lead); resources (lead); supervision (lead); validation (lead); writing – review and editing (lead).Funding was provided by the National Key R & D Program of China (2018YFD0900801).https://github.com/yixian185/data‐for‐ECE.This article has earned Open Data, Open Materials and Preregistered Research Design badges. Data, materials and the preregistered design and analysis plan are available at Appendix S1Click here for additional data file.


Brassica carinata A. Braun) is currently one of the potential oilseeds dedicated to the production for biofuel and other bio-industrial applications. The crop is assumed to be native to Ethiopia where a number of diversified B. carinata germplasms are found and conserved ex situ. However, there is very limited information on the genetic diversity and population structure of the species. This study aimed to investigate the genetic diversity and population structure of B. carinata genotypes of different origins using high-throughput single nucleotide polymorphism (SNP) markers. We used Brassica 90K Illumina InfiniumTM SNP array for genotyping 90 B. carinata genotypes, and a total of 11,499 informative SNP markers were used for investigating the population structure and genetic diversity. The structure analysis, principal coordinate analysis (PcoA) and neighbor-joining tree analysis clustered the 90 B. carinata genotypes into two distinct subpopulations (Pop1 and Pop2). The majority of accessions (65%) were clustered in Pop1, mainly obtained from Oromia and South West Ethiopian People (SWEP) regions. Pop2 constituted dominantly of breeding lines and varieties, implying target selection contributed to the formation of distinct populations. Analysis of molecular variance (AMOVA) revealed a higher genetic variation (93%) within populations than between populations (7%), with low genetic differentiation (PhiPT = 0.07) and poor correlation between genetic and geographical distance (R = 0.02). This implies the presence of gene flow (Nm > 1) and weak geographical structure of accessions. Genetic diversity indices showed the presence of moderate genetic diversity in B. carinata populations with an average genetic diversity value (EH = 0.31) and polymorphism information content (PIC = 0.26). The findings of this study provide important and relevant information for future breeding and conservation efforts of B. carinata.Ethiopian mustard ( B. carinata. A. Braun) (carinata) is an allotetraploid  oilseed crop that has evolved through spontaneous interspecific hybridization between the diploid species Brassica nigra  and Brassica oleracea  [Ethiopian mustard  ,6.In Ethiopia, carinata is cultivated mainly for its seed oil, either for cooking or during the preparation of traditional spicy foods . The useAlternaria leaf spot, makes the crop more suitable in areas where other related crops fail to grow [Carinata has a high potential for use as low-carbon feedstock and in the production of jet biofuel for the aviation industry due to its attractive fatty acid profile, such as high erucic acid (>40%), low content of saturated fatty acids and minimal processing during refining ,12. This to grow ,16.The multi-purpose nature of carinata has attracted breeders’ interest in mining genes responsible for important traits from the available gene pool of the crop. In Ethiopia, there are a number of carinata germplasms maintained in the gene bank of the Ethiopian Biodiversity Institute (EBI) and at research institutes or universities. Most of the germplasms at EBI are landraces or local cultivars, usually called accessions, while advanced breeding lines and released varieties are usually found at national or regional research centers and universities. These germplasms of carinata are important sources of genetic diversity and desirable genes for carinata trait improvement. Prior to the availability of DNA marker technologies, morphological and biochemical markers, including isozymes, were used to assess genetic diversity in crops ,17. AlthCompared with other major crops including oilseeds, there is relatively limited information available on genetic diversity and population structure for carinata using DNA-based molecular markers especially using high-throughput markers like single nucleotide polymorphisms (SNPs) . In earlBrassica species B genome information, has been developed for genetic analysis of allotetraploid Brassica species [B. napus [B. oleracea [For SNP analysis, a genotype array of Brassica 90K Illumina Infinium SNP array, redesigned from a 60K array including 30,000 additional  species ,27. ThisB. napus ,29,30,31oleracea ,33 for gA total of 90 carinata genotypes, including three sets of breeding materials, were used in this study . The fir2/s light intensity, 21/18 °C temperature (day/light) and 65% humidity at Swedish University of Agricultural Sciences (SLU). Genomic DNA of 4–5-week-old plants was extracted using the standard CTAB DNA extraction protocol as described by Clarke [The 90 carinata genotypes were grown in biotron at 16 h photoperiod, 250 µmol/my Clarke  with minBrassica species to construct a hapmap file for further analysis [Markers’ filtration and data imputation were conducted to get informative SNP markers. After removing a total of 23,586 monomorphic markers, SNP positions along with chromosomes were obtained from the reference sequence of analysis ,36. A toanalysis . FinallyPopulation structure analysis was performed based on the filtered 11,499 SNP markers using STRUCTURE software version 2.3.4, which employs the Bayesian model clustering approach . The numD) or expected heterozygosity (EH) and observed heterozygosity (OH) of SNP markers across subpopulations and sub-genomes were analyzed using PowerMarker software version 3.0 [Genetic diversity indices including genetic diversity  of 11,499 SNP markers was determined using PowerMarker software version 3.0 . The PIChttp://github.com/YinLiLin/R-CMplot, accessed on 3 April 2023). The SNP density plot indicated that 11,499 SNPs were distributed across 17 chromosomes, out of which 8 were from B genome and 9 were from C genome  and C (19%) genomes as compared to transversion SNPs, which were 21% and 8.3% in B and C genomes, respectively . AlthougThe 90 genotypes of carinata were grouped into two subpopulations based on the result of STRUCTURE analysis (∆K at K = 2) hereafter referred as Pop1 and Pop2 a,b. Pop1The principal coordinate analysis (PcoA) based on 11,499 SNPs clustered the 90 carinata populations into two subpopulations (Pop1 and Pop2) in similar fashion as that of the STRUCTURE analysis . The firp < 0.001 and gene flow value (Nm) of 6.65  was conducted based on the two distinct subpopulations determined from the STRUCTURE analysis. The analysis results revealed that variations within population accounted for 93% of the total variation, which was significantly higher than variations between populations (7%), as indicated in  of 6.65 .EH) and observed heterozygosity (OH) at individual genome (B or C), whole genome BC and subpopulations (Pop1 and Pop2) levels. The level of expected (EH) and observed (OH) heterozygosity in carinata was found to be 0.314 and 0.176, respectively, at the whole genome level  and C genome . In all cases, the observed heterozygosity was low compared to the expected heterozygosity. In the subpopulations, Pop1 showed almost the same level of EH but a higher level of OH than Pop2 (EH = 0.314), and about 51% of SNPs showed EH above the average value  of 11,499 SNP markers was found within the range of 0.09 to 0.38 with an average value of 0.26 . AccordiB. napus among Brassica species as they were first designed from genomic and transcriptome data of B. napus and its progenitors B. oleracea and Brassica rapa [Brassica B genome could be related to the divergence of B. nigra genome (8 million years ago) prior to B. oleracea (4 million years ago) from their common ancestor, and thus the former species could have accumulated the higher level of mutations [The hybridization arrays are believed to be powerful tools for generating high-quality markers that can be used for various applications in plant breeding. High-density SNP markers arrays were prominent in ica rapa . In thisica rapa  and Khedica rapa . The preutations .B. napus [Brassica juncea [B. rapa L. [Another important finding from the SNP analysis was the frequency of substitution mutations that resulted in the two types of single nucleotide polymorphisms (SNPs), transition and transversion. High frequency of transition SNPs was found over transversion SNPs. This result agrees with the findings of closely related species of carinata such as B. napus ,47,48, Ba juncea , and B.  rapa L. . The cau rapa L. ,51,52.As revealed from STRUCTURE analysis, the 90 carinata genotypes were grouped into two subpopulations (Pop1 and Pop2) whereby the majority of genotypes included in Pop1 were accessions collected from two regions, Oromia (84%) and South West Ethiopian People (SWEP) (80%). This might be due to the fact that those accessions obtained from the two regions share common features or might have been accessed from the nearby districts of the two regions. Interestingly, all the advanced breeding lines and released varieties were found in the other group, Pop2, along with 26 accessions, which are mainly from BNG, SNNP and Amhara regions . The cluAlthough the STRUCTURE analysis showed two distinct subpopulations, there was a low level of pairwise differentiation between Pop1 and Pop2, which was justified by a PhiPT value of 0.07 . AMOVA aEH), describe the proportion of heterozygous genotypes expected under Hardy–Weinberg equilibrium [EH, the higher is variability or diversity of the populations. In the current study, EH value ranged from 0.1 to 0.5 with an average value of 0.314, indicating the presence of moderate diversity in carinata populations. In other similar studies, low to moderate diversity was reported for carinata and its related species B. napus [The genetic diversity indices, expected heterozygosity . Those markers with higher PIC value can be applied in GWAS to mark genes, alleles or haplotypes, which are linked to important quality traits. According to Botstein et al. , the SNPu et al.  and Singu et al. , respect. juncea ,49.p < 0.001). Low differentiation between subpopulations was likely due to the existence of gene flow as justified by Nm > 1 and the Mantel test with a low R-square value of 0.02, indicating weak geographical structure exists among the accessions. Moderate diversity was observed with an average expected heterozygosity of 0.31 and PIC of 0.26. Above all, the finding of this study enhances our understanding of the pattern of genetic diversity in carinata that would be useful for future breeding and conservation efforts of the crop.Single nucleotide polymorphisms (SNPs) are the most abundant markers and highly informative in investigating genetic diversity and genetic relationship among natural populations. Using high-density SNP markers, we were able to explore the genetic diversity and population structure of the 90 carinata genotypes. We employed 11,499 high-quality SNPs that cover 17 chromosomes for population structure and genetic diversity analysis from which two distinct populations (Pop1 and Pop2) were identified that showed higher variations within than between populations as well as low differentiation (PhiPT = 0.07) as revealed by AMOVA (


What is Life?’ that have not, as yet, received sufficient attention in the domain of complexity: delayed entropy, free energy, order out of order and aperiodic crystal. It then demonstrates the important role the four elements play in the dynamics of complex systems by elaborating on their implications for cities as complex systems.This paper draws attention to four central concepts in Schrödinger’s ‘ What is Life  I should have let the discussion turn on free energy instead [italics added]. It is the more familiar notion in this context. But this highly technical term seemed linguistically too near to energy for making the average reader alive to the contrast between the two things. He is likely to take free as more or less an epitheton ornans without much relevance, while actually the concept is a rather intricate one, whose relation to Boltzmann’s order-disorder principle is less easy to trace than for entropy and ‘entropy taken with a negative sign’, which by the way is not my invention.”As Schrödinger indicates, the concept of free energy to which he refers is “a rather intricate one”. It was originally coined by von Helmholtz 1821–1894) in relation to thermodynamics (the theory of steam engines), while at a later stage, it was elaborated by Feynman  in the c in relat“a gas at some temperature contains some inner energy, while gas in a tube with a piston (a steam engine) exerts a force on the piston and can move it doing work. The notion of FE says that only some part of this inner energy can be transformed into work—this part is termed free energy.This law holds quite generally beyond thermodynamics. For example, when we take up food, only part of its inner  energy can be transformed into the work done by our muscles, similarly to electro-chemical processes that require energy. Applied to cities we would say that the maintenance of a city and its dynamics requires energy in form of high quality energy—the FE, which is only part of the energy actually burned in the urban process; the rest is useless heat.”As further shown by , in a mofree energy principle (FEP). More recently, and inspired by FEP, it was related to urban dynamics in the context of synergetic cities  that“… biological agents must avoid surprises…  free energy is an upper bound on surprise, which means that if agents minimize free energy, they implicitly minimize surprise”.To convey this idea, Friston refers to an imaginary creature—a snowflake with wings . In ordeWhat is Life? In a paper entitled “Answering Schrödinger’s question: A free energy formulation”  to maintain their homeostasis” [exploration–exploitation dilemma [As is well recorded, the ostasis” . To corr dilemma , showing dilemma .Despite the ‘E’ in Friston’s FEP, this principle does not refer to the role of energy in such processes, and it is not involved in the “energetic” details. This is apparently due to the very complicated pathway in life processes, from the elementary energy processes to the final result, in the form of muscle contraction, sensations and the like. As a model that employs mathematical tools from physics, FEP, thus, deals with relations between macroscopic features, e.g., perception and action in the case of cognition, or in the context of cities, the relations between the number of citizens and income. For details, see , while cognitively by means of the routinized and reproductive behavior of urban agents that minimizes surprise, thus avoiding cognitive phase transition (as described in niche construction theory (NCT) [It is interesting to note that, in recent years, the process of niche construction has been suggested as an integral component in standard evolutionary processes such as natural selection. This view, termed ry (NCT) ,18, entary (NCT) . Can NCTCities are hybrid complex systems composed of artifacts (objects constructed by humans), which are simple systems, and humans as complex systems ,20,21. NThe Sciences of the Artificial, that artifacts designed and made by humans are complex systems. Yet, this is not the case—there is no contradiction here: complexity theory started by reference to phenomena of material systems, such as the Bénard cells or the LASER. This physicalist approach was successfully applied to organic and social systems that were also shown to be complex systems. However, at a later stage, the studies of Gell-Mann [adaptive systems . In a similar way, it has been demonstrated that while cities are complex systems and CASs, they still differ from the latter two in also being hybrid complex systems, as defined above. It must be emphasized that cities are but one example of a much larger class of hybrid complex systems, an issue that extends beyond the context of the present study.On the face of it, the above view that the artificial components of cities as hybrid complex systems are simple systems contradicts previous views about artifacts, specifically Simon’s  view in ell-Mann ,24 and Hell-Mann  demonstrimpermanence implied by Schrödinger’s view of life follows human thinking from early times. However, while there is no room here to delve into this topic, it is interesting to note that the impermanence of cities as complex systems, as above, entails a dilemma: how would we explain, for instance, firstly, the existence of the complex system ‘city’ for more than 5000 years now, and secondly, the differences between the various cities that existed in these thousands of years? The answers come from Schrödinger’s ‘order from order’ principle.As noted above, the property of order from order:Schrödinger’s ‘order out of disorder’ principle anticipated the ‘order out of chaos’ motto of complexity theories. Yet, Schrödinger considered the latter principle as secondary, suggesting that life springs from another principle that he termed The orderliness encountered in the unfolding of life springs from a different source. It appears that there are two different ‘mechanisms’ by which orderly events can be produced: the ‘statistical mechanism’ which produces order from disorder and the new one, producing order from order.While the first is in line with the laws of physics, continues Schrödinger, it is not sufficient to account for life:But we cannot expect that the ‘laws of physics’ derived from it suffice straightaway to explain the behaviour of living matter, whose most striking features are visibly based to a large extent on the ‘order-from- order’ principle.The ‘order from order’ principle follows the observation that “… the gene generated order from order in a species, that is, the progeny inherited the traits of the parent”. Cities as complex systems provide a par-excellence case of order from order, namely, that the “progeny city” inherited the traits of the “parent city”.Cities, as is well recorded, have existed for more than 5500 years now. During this time, other social formations, such as empires, kingdoms, clans, tribes…, emerged, declined and disappeared, while cities not only still exist but have become the main form of settlement and life. During these last 5500 years, cities have changed from generation to generation, from culture to culture and from place to place, when the principle and engine of their change has not been order out of chaos (the title of ) but ratcultural evolution—a process that attracted intensive research [Cultural Transmission and Evolution: A Quantitative Approach. Commencing from a neo-Darwinian point of view, they propose that just as biological evolution is governed by genetic transmission, so cultural evolution is governed by cultural transmission. However, Cavalli-Sforza and Feldman (ibid) further suggest that in order to apply the neo-Darwinian perspective to cultural evolution, one has to take into consideration one difference between the two transmission processes: Unlike biological transmission, which occurs between the parent and child of successive generations only, in the cultural domain, transmission takes place between the parent and child of successive generations, between neighbors and between neighbors of two successive generations. These they termed, respectively, vertical, horizontal and oblique transmissions. Similar to “copying mistakes”  in genetic transmission, in culture, a new cultural trait is born out of learning mistakes. Finally, as a genetic mutation becomes subject to environmental selection, so a newborn cultural mutation becomes subject to selection by the “cultural environment” that determines its fate to extinction or spatial diffusion. As an example, Cavalli-Sforza and Feldman reconstructed the spatial diffusion of agriculture (left). In right, the spatial diffusion of urbanism was added.Cities, as complex hybrid systems, are composed of artifacts (simple systems) and humans (complex systems). Now, artifacts of all kinds, including tools, machines, cars, buildings, roads and whole cities, are subject to research . Of specresearch  study Cuiculture , left. Ipace of the two processes, as just discussed, the role of memory and imagination in the process and the question of code, namely, do artifacts have a cultural code similar to the genetic code of organic entities? The first two issues are discussed next, while the third is discussed in However, cultural evolution differs from biological evolution in three additional fundamental (and interrelated?) respects: the Compared to biological evolution, cultural evolution  is an extremely fast process. While fast, there are, however, considerable time differences between the various processes of cultural evolution. Thus, the spatial diffusion of agriculture and urbanism  are on turban revolution. It is also commonly agreed that this event followed the collapse of the Neolithic agricultural society that preceded it. This Neolithic culture dominated for some 5000 years since the so-called Neolithic revolution around 10,000 years B.P. and then collapsed and was replaced by the emerging urban society. On the face of it, the event of the urban revolution must be considered a case of an emerging new order out of disorder, since the collapse of the Neolithic era with its agricultural society was likely to be associated with a chaotic period. Indeed, the various theories about this event agree that the Neolithic period ended up with a major crisis and chaotic reality, yet they disagree and differ in their suggestions regarding the factors that brought about the collapse. According to Childe’s (ibid) Marxist view (or rather speculation), neolithic society collapsed as a consequence of the urban revolution, itself a logical consequence of the neolithic mode of production: food production entailed surplus and specialization, which gave rise to a stratified society and the emergence of urbanism. More recent studies suggest that the cause might have been a large-scale plague [However, what about the first city? What principle gave rise to its emergence some 5500 years ago? It is commonly agreed that cities and urban society came into being via an event termed by Gordon V. Childe  as urbane plague ; as juste plague ,32.The evidence about the emergence of urban society some 5500 years ago is often associated with the implicit or explicit view that cities and urbanism were a new invention—an outcome of the phase transition between the neolith and urbanism. However, as recently shown , archaeolongue durée of human settlement, for instance, is characterized by long periods of steady state interrupted by short periods of chaos, phase transition and the emergence of a new order and a new steady state and so on. Thus, assume that in the upper part of In light of the above, the urban revolution of 5500 years ago can also be considered a case of order from order. Therefore, do we have to give up the order out of chaos principle? No! The two principles co-exist. The “… let me anticipate”, writes Schrödinger, “what will be explained in much more detail later, namely, that the most essential part of a living cell-the chromosome fibre may suitably be called an aperiodic crystal. … which, in my opinion, is the material carrier of life.” By the notion of aperiodic crystal or solid, Schrödinger refers to an ordered and organized, but non-periodic, structure of chromosomes that contain “… in some kind of code-script the entire pattern of the individual’s future development and of its functioning in the mature state. Every complete set of chromosomes contains the full code.” He then concludes:We might quite properly call that an aperiodic crystal or solid and express our hypothesis by saying: We believe a gene -or perhaps the whole chromosome fibre -to be an aperiodic solid.Less than 10 years later, in 1953, inspired by Schrödinger’s brilliant intuition, Cambridge University scientists James D. Watson and Francis H.C. Crick announced that they had determined the double-helix structure of DNA, the molecule containing human genes. “Watson and I” wrote Crick to Schrödinger on the occasion of his 66th birthday, (12 August 1953),“… were once discussing how we came to enter the field of molecular biology, and we discovered that we had both been influenced by your little book ‘What is Life?’… We thought you might be interested—you will see that it looks as though your term ‘aperiodic crystal’ is going to be a very apt one.”  aperiodic crystal or solid? Apparently, it was to emphasize the way it differs from a regular (periodic) crystal, whose growth is characterized by periodicity in which a given motif (a unit cell) repeats itself again and again in 3D space. An aperiodic crystal or solid, per contra, lacks this strict periodicity. Compared with the aperiodic crystal, writes Schrödinger, periodic crystalsare rather plain and dull. The difference in structure is of the same kind as that between an ordinary wallpaper in which the same pattern is repeated again and again in regular periodicity and a masterpiece of embroidery, say a Raphael tapestry, which shows no dull repetition, but an elaborate, coherent, meaningful design traced by the great master.As noted by Varn and Crutchfield ,… exact repetition of a motif, in other words a crystal, is information poor—too poor to carry heredity. Without some unpredictability, or novelty, nothing new is learned and communicated. It is remarkable that Schrödinger made this prediction before a quantitative understanding of information was articulated.longue durée of settlement and cities, as above, that are characterized by long periods of steady state (little change) and short events of phase transition (drastic major change).Schrodinger’s aperiodic crystal, thus, emphasizes unpredictability, impermanence and novelty. On the other hand, however, as we have seen above, Friston’s FEP emphasizes the exact opposite—the tendency of complex living systems to maintain a steady state. How can these two perceptions and properties be reconciled? Can they be reconciled? The answer is positive if we look at the The genetic code is a property of living systems, and living systems are complex systems. Yet, not all complex systems are living systems and, as such, need not have a genetic code. What about cities as complex systems?One of the surprises that followed the finding of the genetic code, the structure of DNA and the possibility to measure genetic distances, concerns the degree of similarity within and between species: 99.9% of your DNA is identical to that of any other human being, while 98.8% of our DNA is identical to that of chimpanzees. We humans are not so different from each other and from other animals as we tend to think and experience. Can this be applied to cities? Is there a genetic code of all cities? Can we say that the similarity between the cities of 5000 years ago is 99%, 98% or even 70% identical to the cities of today? According to Hillier  the answIn this paper, Hillier uses his space syntax formalism as a means to explore the possibility that there exists a universal “genetic code” for cities that will provide the foundation for “a theory of a universal city underlying cities in general…” Hillier’s space syntax, as developed by him in the last few decades, refers specifically to the functional and spatial structures of cities and the relation between them . CommencHillier then notes that space syntax studies brought to light two phenomena that, together, according to him, make up the genetic code of cities: spatial emergence and spatial agency. Spatial emergence is an elaboration of the structuralist view of the city as a representation of socio-economic processes. However, in place of the structuralist view that space  is a representation of socio-economic processes, he suggests that socio-economic processes give rise to specific acts of building that, in turn, shape the urban space. By spatial agency, Hillier refers to the functional ‘from space to society’ processes by which the structure of the emerging space affects, for instance, movement flows that, in turn, affect land use (as proved time and again by space syntax) and transform the city from a collection of independent structures to a living city.“It is these two linked processes of spatial emergence and spatial agency that set in train the self-organising processes through which cities acquire their more or less universal spatial form.” What these self-organizing processes are is not specified in the paper, which, as Hillier emphasizes, is not as yet a fully fledged theory. However, in concluding his paper, Hillier expresses optimism that it “seems reasonable to advance the suggestion than that by expressing the complex processes of self-organisation through which cities come into existence as both spatial and functional systems, in terms of two simple, mathematically expressible laws, we are likely to be close to formulating the principles of a genetic code for cities. It is of course far from complete, and above all in need of a general mathematical treatment”.Hillier uses the notion of the genetic code metaphorically, of course, as cities are not a consequence of biological information caried by polymers composed of polynucleotide chains that coil around each other to form the double helix. Can such a metaphor be justified? In light of the above discussion, it can be said that Hillier refers, in fact, to what we have described above as cultural code. If so, it is relevant to note here, firstly, that the cultural analogy to genes and genetic code was already suggested and developed by Dawkins  and otheThere is no room here to further elaborate on the above applications; however, what is important to emphasize in the present context is the lesson from the above applications: namely, that the analogy between genes and memes is only partial. As already noted above, memes are not genes. They are not innate nor are they associated with a specific material entity such as DNA molecules. Rather, they are products of a complex interaction between internal brain entities—memes and their external manifestations in the form of bodily activities. As such, unlike genes, they quite often represent natural and artificial objects, categories and other entities and external representations that exist in the world. However, most importantly, the relations between genes and their external representations  are asymmetric. In biological evolution, genotypes give rise to externally represented phenotypic effects, but the latter cannot be transformed back into genotypes. Memes and their externally represented (“phenotypic”) effects, per contra, co-exist in symmetric relations. An idea about a circle can generate a circle in the world, while an object in the world, say a shelter, can generate the memes ‘home’ or ‘house’. This is the case with respect to a house as a single artifact, and this is the case with respect to a group of houses that forms the meme ‘neighborhood’ and, on a larger scale, the meme ‘city’.Why the question mark? Why doubt the possibility that, similarly to organisms, cities too have a code, if not genetic then memetic? This is the case, since the view of the city as a kind of organism accompanies the study of cities from the first day urbanists began to map and describe cities ,39. Partfamily resemblance and subsequent cognitive theories of categorization challenge and question this view with respect to categories in general, while in several past studies [Both notions—genetic and memetic codes—imply that at the core of each member of a category , there is a common code that is shared by all members of that category. In the case of cities, it implies that all cities from the last 5500 years must share a common set of properties that form their genetic or memetic code. Wittgenstein’s  notion o studies ,41, it hPhilosophical Investigations, Wittgenstein [In paragraph 66 of his genstein  uses thegenstein ,41, citiembodied cognition approach—an approach that challenged the domination of the classical cognitivism approach by variously rejecting the brain as a computer by emphasizing the significance of an agent’s physical body in cognitive abilities. Thus, Rosch et al. [prototypical so that an orange, for instance, is “more” of a fruit than a melon. Applied to cities [Wittgenstein’s “family resemblance” was adopted by cognitive scientists as a starting point for a discussion about the processes of categorization and concept formation, specifically in connection with the h et al.  added tho cities ,41, whilinformation processing approach that has dominated cognitive science since its birth in the mid-1950s, with the view that cognition is essentially an algorithm executed on the hardware of the brain, principally independent of the body and its environment. In contrast to this view, proponents of embodied cognition have demonstrated that cognition is embodied. They have done so from a variety of perspectives ranging from psychology and linguistics, to neurology with direct association to complexity and self-organization. . iews see  “experiea huge family resemblance network of connected similarities. In this evolving and diffusing network, one can identity core and periphery: cities, images, and urban phenomena, which in a certain space-time moment capture the central area of the net as the most typical or prototypical exemplars as well as cities that form the periphery. They are all related to each other by means of imagination, that is, similarities, metaphors and family resemblances. Cities looked upon from this perspective have been termed “The cities of experiential realism” ([ealism” (, ChapterWhat is Life?’ that have not, as yet, received sufficient attention in the domain of complexity: delayed entropy, free energy, order out of order and aperiodic crystal. Secondly, the aim was to demonstrate the important role the four elements play in the dynamics of complex systems by elaborating on their implications for cities as complex systems. Accordingly, the discussion in this paper was essentially introductory; namely, each section shortly introduced one of the four concepts and indicated its potential. The next step would be to elaborate on each of the above four notions and materialize their potential in full, that is, firstly, the potential delayed entropy to shed light on the constructive and destructive dimensions of a system’s interaction with its environment, as well as on its association with the notions of impermanence and time–geography and, secondly, the potential of free energy to shed light on the processes of steady state and phase transition in complex systems and in cities as such, and the potential to link the niche construction theory to urban dynamics. Thirdly, the principle of order from order should be considered and its relations to processes of space–time diffusion and the role of memory in them. Finally, the concept of aperiodic crystal entails a whole set of questions regarding whether cities have a genetic code, or a memetic code, or similarly to Wittgenstein’s family resemblance categories, they do not have a code at all. Fascinating as they are, materializing the above potential and answering the above questions will have to await further research.The aim of this paper was, firstly, to draw attention to four concepts developed by Schrödinger in his ‘