To ascertain the reliability of Common Blue as a source of photonic nanostructures, we compared the structural colour of the laboratory population to different natural genetic lineages. Although clear signs of genetic erosion due to inbreeding was detected through molecular genetic variability analysis, we observed minimal differences in the structural colouration showing the exceptional stability of the photonic nanostructures in the wing scales of Common Blue males. Consequently, there is a notable economic opportunity in developing technology that can be applied in industry to produce these natural materials that may be used directly in different kinds of applications requiring intricate photonic nanostructures.This study introduces a cost-effective and environmentally friendly laboratory procedure and infrastructure that can be readily applied to industrial production. We demonstrate that a custom-made insectarium can yield natural, finely tuned photonic nanostructure surfaces year-round using Common Blue butterfly laboratory populations. Using a single device, this process enables the production of more than 7500 wing samples annually, equivalent to up to 1 mPolyommatus icarus) populations. They are promising templates of future artificial photonic materials and can be used in potential applications, too. In this work, we present methodology and infrastructure for breeding laboratory populations of Common Blue as a cost-effective and environmentally friendly source of nanostructures. Our technology enables the production of approximately 7500 wing samples, equivalent to 0.5–1 m2 of photonic nanoarchitecture surface within a year in a single custom-made insectarium. To ascertain the reliability of this method, we compared reflectance properties between different populations from distant geographic locations. We also provide genetic background of these populations using microsatellite genotyping. The laboratory population showed genetic erosion, but even after four generations of inbreeding, only minimal shifts in the structural colouration were observed, indicating that wild Common Blue populations may be a reliable source of raw material for photonic surfaces.The colour of the butterfly wing serves as an important sexual and species-specific signal. Some species produce structural colouration by developing wing scales with photonic nanoarchitectures. These nanostructures are highly conservative, allowing only a ±10 nm peak wavelength deviation in the reflectance spectra of the blue structural colour in natural Common Blue ( The search for technical solutions in the organic world has deep roots lost in the early times of human history. For example, almost all the cultures discovered that the scaled surface of various reptiles may work as a protective shield, hence the helms of the warriors mimicked crocodile or turtle armour, and as the applied technology advanced the material, they turned from animal skin to steel wire. Similarly, all the cultures discovered the efficient manner of pelt in thermal regulation, which was used for various isolation techniques, but finally was replaced by synthetic materials. Therefore, the bio-mediated structures are copious sources for the innovation of human technology .Apis mellifera . O. O4) hadof 38.7% B compareThe population genetic structure of the four groups was investigated. Although nuclear microsatellites or single sequence repeats (SSRs) are in the non-coding part of the DNA, as they are known to have exceptionally high mutation rate , they ar4) from the rest. Between the natural populations, the difference is apparently proportional to their geographic distance. In terms of absolute values, mean Gst between the natural populations was only 0.037, whereas the differentiation of the laboratory population from the natural ones was 0.2234.Similarly, genetic differentiation is remarkable B: the poThis high level of genetic differentiation in the laboratory population must be associated with the combined effect of (i) using three pairs as founders of the captive population (founder effect) and (ii) four generations of inbreeding. These two factors led to severe genetic drift in the laboratory population compared to the natural ones. The above process is further demonstrated by the genetic diversity values : the labIn contrast to the population-level analyses, the phylogenetic origin of the laboratory population  remained unaffected . The labIn summary, our laboratory population that was kept for four generations in captivity shows all population genetic characteristics described for small, inbred populations usually studied in the context of conservation . HoweverArhopala that covers the spectral range from the UV to green [Morpho butterflies, we were able to increase the catalytic efficiency of the butterfly wings covered by a conformal 20 nm thin layer of ZnO by about ten times as compared to a flat glass substrate [Polyommatus icarus wings showed that the Au nanoparticles incorporated into the photonic nanoarchitecture of the butterfly wings exhibited hybridization between the photonic nanoarchitecture and the metallic nanoparticles with plasmonic properties [As we reported recently in several papers, various butterfly wings possessing structural colour were successfully used as biotemplates with complex nanoarchitecture for the deposition of thin films with photocatalytic properties ,50. The to green . In our operties . These f2 of structurally blue coloured surface annually that may be used directly in different kinds of applications requiring intricate photonic nanoarchitectures.The Common Blue can be turned into a laboratory insect to produce high-quality photonic nanoarchitectures, as a large number of samples with the same optical properties can be achieved which are not sensitive to inbreeding. The reproducibility of the structure and colour of such nanoarchitectures might be governed by a precise, genetically controlled self-assembly mechanism of scale growth. At the same time, the process of obtaining the well-reproduced photonic nanoarchitectures is based on the breeding of herbivorous insects, which does not raise environmental concerns and needs only moderate amounts of energy input and work hours of highly qualified operators. We demonstrated using our custom-made insectarium that ready-made prototypes of finely tuned photonic nanostructure surfaces can be yielded year-round using Common Blue laboratory populations in a cost-effective and environmentally friendly manner. Our breeding system is scalable. Using a single insectarium, we were able to breed 500–700 butterflies every two months, which in summary, can result in approximately 7500 optically highly similar wing samples together constructing 0.5–1 m


To enhance and extend the knowledge about the global historical and phylogenetic relationships between Merino and Merino-derived breeds, 19 populations were genotyped with the OvineSNP50 BeadChip specifically for this study, while an additional 23 populations from the publicly available genotypes were retrieved. Three complementary statistical tests, Rsb (extended haplotype homozygosity between-populations), XP-EHH (cross-population extended haplotype homozygosity), and runs of homozygosity (ROH) islands were applied to identify genomic variants with potential impact on the adaptability of Merino genetic type in two contrasting climate zones.Ovis aries chromosomes (OAR) 1, 6 and 16, whereas two genomic regions on OAR6, that partially overlapped with the previous ones, were highlighted by ROH islands. Overall, the three approaches identified 106 candidate genes putatively under selection. Among them, genes related to immune response were identified via the gene interaction network. In addition, several candidate genes were found, such as LEKR1, LCORL, GHR, RBPJ, BMPR1B, PPARGC1A, and PRKAA1, related to morphological, growth and reproductive traits, adaptive thermogenesis, and hypoxia responses.The results indicate that a large part of the Merino’s genetic relatedness and admixture patterns are explained by their genetic background and/or geographic origin, followed by local admixture. Multi-dimensional scaling, Neighbor-Net, Admixture, and TREEMIX analyses consistently provided evidence of the role of Australian, Rambouillet and German strains in the extensive gene introgression into the other Merino and Merino-derived breeds. The close relationship between Iberian Merinos and other South-western European breeds is consistent with the Iberian origin of the Merino genetic type, with traces from previous contributions of other Mediterranean stocks. Using Rsb and XP-EHH approaches, signatures of selection were detected spanning four genomic regions located on To the best of our knowledge, this is the first comprehensive dataset that includes most of the Merino and Merino-derived sheep breeds raised in different regions of the world. The results provide an in-depth picture of the genetic makeup of the current Merino and Merino-derived breeds, highlighting the possible selection pressures associated with the combined effect of anthropic and environmental factors. The study underlines the importance of Merino genetic types as invaluable resources of possible adaptive diversity in the context of the occurring climate changes.The online version contains supplementary material available at 10.1186/s12711-023-00797-z. Ovis aries) are one of the oldest livestock species, i.e. they were domesticated by humans around 10,500 years before the present (YBP) in the Fertile Crescent (South-eastern Anatolia and the Iranian Zagros Mountains)  and pXP-EHH = − log10[1–2|Φ(XP-EHH) − 0.5|], where Φ (x) is the Gaussian cumulative distribution function. To detect signatures of selection, the genome was split into 250-kb sliding windows that partially overlapped 10 kb with adjacent windows. A window is classified as putatively under selection when it contains at least three SNPs exceeding the significance threshold of − log10  = 4.The analyses were computed using the age in R . Haplotyage in R . Since f package  was usedGenomic regions that were characterised by a high frequency of ROH occurrence (ROH islands) were identified. To do this, the number of times each SNP occurred in a ROH was considered and normalised by dividing it by the number of animals included in the analysis. The top 0.999 SNPs of the percentile distribution of the locus homozygosity range within each group were considered as potential ROH islands, as suggested in previous studies , 59.Homo sapiens datasets. This tool enables the construction of weighted interaction networks, which use as a source a very large set of functional association data including protein and genetic interactions, pathways, co-expression, co-localisation, and protein domain similarity. For a more detailed description of the considered network categories see [Genomic regions identified by the three approaches for detecting signatures of selection were interrogated for genes annotated to the OAR v4.0 genome assembly using the Genome Data Viewer provided by NCBI . To inveries see .During quality control of the initial raw dataset, 44 individuals with low-quality genotypes and six duplicated individuals were excluded. Thus, the working version of the dataset included 1644 animals and 42 populations with an average population size of 40.26 and a size ranging from 10  to 105 . All the animals included in the analysis had high-quality genotyping. Additional file Descriptive statistics of the genetic diversity are in Table Ne13) ranged from 114 (Groznensk—GRZ) to 48 (RAM) and a similar trend was found for the Ne50 values, with the highest value for GRZ (350) and the lowest for RAM (77). However, SNeP analysis identified a marked reduction in Ne from 50 to 13 generations back for the GDP, GRZ, Paska (PAK), SOP, MBB, Merino Preto (PRE), SME_FB, SME_TP, and Turkish Merino (TKM) breeds. The decline in Ne for RAM, Arapawa (APA), and Polish Colored Merino (PCM) was less steep compared to that for the other breeds, probably due to their small population sizes  to 89.80 (MCM) , followed by RAM (0.237) and APA (0.134), while the lowest values are observed in the Russian breeds  and in TKM (0.010).Individual genomic inbreeding was evaluated using ROH analysis, as reported in Table To examine the genetic relationships between breeds, MDS plots of the pairwise IBS distances Fig.  were genThe first dimension of the MDS plot accounted for 3.40% of the genetic diversity, the second dimension for 3.15%, and the third dimension for 2.27%. In this analysis, three principal clusters according to breed genetic background or geographical proximity are highlighted. All the Australian breeds  clustered together and near the South American breeds (MAR and Merino Uruguay—MUR) and the South Africa Merino sheep (SAM). Chinese Merino (CME), American Rambouillet (RMB), Polish Merinos , HUG, SMM, and FLE clustered close to each other. All the other European Merino and Merino-derived sheep clustered together, except for Merinizzata Italiana (MEI) and TRS. These last two breeds are known to have been influenced by the IDF and MLA breeds, and indirectly by the Berrichon du Cher (BDC) breed, as confirmed by their position in the MDS plot close to these French breeds. Other breeds  were scattered over the plot. The MDS plot representing single animals  and ranged from 0.004 to 0.411, with the closest pair-wise value (0.004) observed between the Australian AIM and AUM breeds. RAM and MCM sheep are the most differentiated breeds, probably due to strong genetic drift effects.The relationships between the studied sheep breeds were assessed by calculating a pairwise To further investigate the genetic relationships among the studied Merino sheep breeds, a Neighbor-Net graph was constructed using the Reynolds’ distances Fig. .Fig. 2NeIn Fig. To determine the proportion of shared ancestry genomic components, Admixture analysis on the whole dataset was done to separate breeds according to their genetic background and/or geographic origin Fig. .Fig. 3AdK = 43, suggesting that this value is the most probable number of clusters explaining the variation in this dataset . The two Corriedale (COA and COU) are separated at K = 3, and their cluster (in red) is mainly shared with BDC and IDF, while it is poorly shared with the three Australian breeds, the Argentinean, the Uruguayan and the South African Merino and Merino-derived breeds. These breeds clearly appear, from K = 3 to K = 5, as a rather homogeneous group, well differentiated from the others. At K = 4, a new cluster (in green) appears, which represents mainly some European Merino-derived breeds , the Turkish Merino (TKM), the Chinese Merino (CME), the two South African breeds (SMM and Dohne Merino—DHM) and the North American RMB.The cross-validation error registered the lowest value at K = 5 was highly consistent with the Neighbor-Net graph. As expected, the genomic architecture reflected both the geographic origin and the genetic background of each breed, with some unique genetic signatures, such as those observed for the South-western European breeds, the South African breeds, and the South American improved breeds. Other breeds, such as the APA, displayed a clear sub-structuring. At K = 43, several breeds showed a complex admixture-like pattern with a mosaic of different genetic components  to 15.15 Mb (on OAR6), were identified by both the Rsb and XP-EHH approaches , and their main biological functions are presented in Additional file Six genomic regions that frequently appeared in a ROH were identified in the two groups. Additional file The results of the gene network analysis for the genes located in the putatively selected regions mentioned above are presented in Additional file Ovis aries) bred in the world, the Merino, developed in the Iberian Peninsula, mainly in Spain [It is assumed that the first documented sheep   and non-registered (SME_TP) animals, a high similarity to the Portuguese Merino Branco, Merino de Beira Baixa and Preto, and a central position (in the MDS plot) supporting its role as a base of the European South-western breeds. The two Spanish sample batches clustered together in the Neighbor-Net graph Fig. , showingnces 0.00  and similar to the values reported in southern and western European sheep breeds , 19. A lThe results of the MDS plot, Neighbor-Net, and Admixture analyses are consistent with known breeds histories and broad geographic classifications. The MDS analysis shows a clear gradient along the first component Fig. : Fig. S2Neighbor-Net and Admixture graphs suggest a common ancestry between Australian fine-wool breeds, South American , Eastern Europe (Russian and Romanian) and South African sheep Figs.  and 3. AIn Eastern Europe, most of the Russian fine-wool breeds were developed during the Soviet period by crossing local breeds with Australian Merino and American Rambouillet rams , 69. TheThe history of the South African Merino stretches back to 1789, when the Dutch Government donated two Spanish Merino rams and four Spanish Merino ewes to Col. Jacob Gordon, the military commander in the Cape at that time. The introduction of other Merino strains, most notably those from Australia, occurred during the last 200 years . MaximumIn the Neighbor-Net graph Fig. , SMM cluIn Turkey, the TKM breed shows a pattern of admixture that was quite similar to that observed for DHM, which supports the hypothesis that they share a German common ancestor Fig. . In factBoth Neighbor-Net Fig.  and AdmiIn the MDS plot and the Neighbor-Net graph Figs.  and 2, tFST values  highlight a pattern of admixture that is quite similar Fig. : Fig. S5CCSER1, SLIT2, PACRGL, and KCNIP4), which are known or assumed to be involved in inflammatory and immune responses (SLIT2 and KCNIP4) and in growth traits  . These regions include four genes . The most supported gene function (“Pore complex”) has been also clearly demonstrated to play a role in antiviral innate immunity [Complement activation” is known to represent a crucial mechanism in the innate defence against common pathogens [Integral component of plasma membrane”. Integral membrane proteins have been assigned a broad range of functionalities, which include roles as transporters, linkers, channels, receptors, enzymes, structural membrane-anchoring domains, proteins involved in accumulation and transduction of energy, and proteins responsible for cell adhesion. Also among these proteins are toll receptors, which are known to play an essential role in the recognition of microbial components [Positive regulation of protein serine/threonine kinase activity” was also found among the detected gene functions. Serine/threonine kinases play a role in the regulation of cell proliferation, apoptosis, cell differentiation, and embryonic development. Moreover, nuclear Dbf2-related (NDR) serine/threonine kinases have been shown to regulate cytokine-induced inflammation [Negative regulation of leukocyte migration” was another supported gene function inferred from the gene interaction network. Leukocyte migration is a fundamental immune response that is a prerequisite to the entry of effector cells such as neutrophils, monocytes, and effector T cells to sites of infection [Overall, the three approaches identified more than 100 genes that were putatively under selection in the considered sheep groups. The resulting gene interaction network pointed to gene functions that are related to immune response  have mainly experienced differential environmental-driven selection pressure. This would also be consistent with the fact that, overall, candidate genes related to metabolism, immunity, hypoxia, and temperature were found in this study .Additional file 3: Table S3. Descriptive statistics and classification by length of the ROH for each Merino and Merino-derived sheep breed.Additional file 4: Figure S1. Distribution of ROH inbreeding coefficients for each Merino and Merino derived breed.Additional file 5: Figure S2. MDS plots of Dimensions 1 vs 2 (panel a) and 1 vs 3 (panel b). Each point represents a single individual. The correspondence between breeds and symbols is given in the legend box in the upper right corner. For full definition of breeds see Table Additional file 6: Table S4. Matrix of pairwise estimates of FST statistic  and pairwise Reynolds' distances between breeds .Additional file 7: Figure S3. Heatmap of pairwise FST values. For full definition of breeds see Table Additional file 8: Figure S4. Cross-validation (CV) error values (a) and number of iterations required to reach convergence (b) calculated through Admixture software runs for K values ranging from 2 to 46. The black arrow indicates the K = 43 value with the lowest CV score.Additional file 9: Figure S5. Admixture analysis plot in a circular fashion with all values of K (number of clusters) ranging from 2 to 43. For full definition of breeds see Table Additional file 10: Figure S6. Optional number of migration events in the complete dataset calculated by using the “plot_optM” function in the R package OptM. (a) The mean and standard deviation (SD) for the composite likelihood L(m)  and proportion of variance explained . The 99.8% threshold is that recommended by Pickrell and Pritchard [ritchard , but notAdditional file 11: Table S5. Overlapping genomic regions identified with both the Rsb and XP-EHH approaches in the comparison between sheep reared under Mediterranean vs Continental climate.Additional file 12. Description of genes under divergent selection identified by Rsb and XP-EHH approaches.Additional file 13: Table S6. List of identified genomic regions of extended homozygosity (ROH islands).Additional file 14. Description of genes detected by ROH in the overlapping regions with those identified by Rsb and XP-EHH approaches.Additional file 15: Figure S7. Gene network produced by using GeneMANIA. The genes of interest are represented as stripped grey circles, and related genes as plain circles. Co-expressions are displayed as violet lines, physical interactions as red lines, shared pathways as light blue lines, genetic interaction as light green lines, and co-localisations as blue lines.Additional file 16: Table S7. Description of gene functions highlighted in the GeneMANIA network.


Genetic indicators of animal populations are an important part of the parameters used to describe the population status. In addition, for social species, such as the grey wolf, the preservation of typical kinship relations and social structure is important to ensure the existence of populations as a well-functioning part of an ecosystem. Hunting can affect the genetic parameters and the stability of social structures. In this study, muscle tissue samples from wolves hunted in Latvia were collected between 2009 and 2021 for genetic and kinship analyses. It was established that the hunting pressure during this time had not caused negative changes in the genetic parameters of the population. The typical pack structure was observed; however, negative consequences of hunting were found in breeder loss, pack disruption, a loss of pack territories, and the early dispersal of juveniles. Although similar processes also occur in unexploited wolf populations, they affect the social structure of the population in less disruptive ways than the impact of high hunting pressure. Therefore, the impact of hunting on the kinship and social structure of the population should be further monitored and the consequences that such disturbances may have on the conservation of the population should be clarified.Nowadays, genetic research methods play an important role in animal population studies. Since 2009, genetic material from Latvian wolf specimens obtained through hunting has been systematically gathered. This study, spanning until 2021, scrutinizes the consequences of regulated wolf hunting on population genetic metrics, kinship dynamics, and social organization. We employed 16 autosomal microsatellites to investigate relationships between full siblings and parent–offspring pairs. Our analysis encompassed expected and observed heterozygosity, inbreeding coefficients, allelic diversity, genetic distance and differentiation, mean pairwise relatedness, and the number of migrants per generation. The Latvian wolf population demonstrated robust genetic diversity with minimal inbreeding, maintaining stable allelic diversity and high heterozygosity over time and it is not fragmented. Our findings reveal the persistence of conventional wolf pack structures and enduring kinship groups. However, the study also underscores the adverse effects of intensified hunting pressure, leading to breeder loss, pack disruption, territorial displacement, and the premature dispersal of juvenile wolves. In the last two decades, the use of molecular genetic methods has become widespread in studies of animal species and in addressing conservation issues ,2. The cIn Latvia, the wolf is a specially protected species of limited use. Before Latvia’s accession to the European Union and the implementation of the Habitats Directive 92/43/EEC of the Council of Europe, wolves were hunted without any restrictions, and at the beginning of the 2000s, the number of predators significantly decreased. In 2000, the development of the first wolf species conservation plan was started in Latvia , and sinWolves mostly live in packs consisting of related animals—a pair of breeding animals and their offspring ,42. It iThe aim of this study is to describe several genetic characteristics and the kinship structure of the restrictedly harvested wolf population in Latvia and to assess the effect of current hunting practices on the genetic parameters, social and territorial structure, and the possibilities for the sustainable conservation of the population.2 along the eastern coast of the Baltic Sea. Wolves are distributed and harvested in the whole country with the exception of the territory around the capital Riga. Approximately 50% of the region is covered with mainly mixed boreal forests. A hunting season from 15 July to 31 March and an annual hunting quota were introduced in Latvia in 2004. The quota is set annually for the whole country. It has been increasing since 2004 and was around 270–300 animals for the last ten hunting seasons. Official census data in the last decade have estimated the number of wolves in Latvia to be around 1200 individuals, while Virtual Population Analyses estimates of minimum wolf abundance for the same period were around 700–800 individuals. Hunting mortality was estimated to be about 37% in the last two decades [Capreolus capreolus) and wild boar (Sus scrofa), and prey availability during the study period was not a limiting factor for the population [Material and data for this study were collected throughout the entire territory of Latvia, which occupies 64.6 thousand kmpulation .Data and material were gathered from legally hunted or otherwise killed (traffic accidents and scabies) wolves throughout the country in the framework of an ongoing wolf monitoring program. For genetic analyses, we collected 1363 muscle tissue samples between 15 July 2009 and 31 March 2021. A total of 1269 individuals  were successfully genotyped  accountiThe date and location of hunting were known for each hunted wolf. Since 2015, the coordinates of the hunting location had been marked, and prior to that, the parish where the individual was hunted. We calculated straight line distances of movements of related individuals between centres of parishes where animals were harvested.® Blend Master Mix  concentration of 2 mM), and 0.2 μM of forward (labelled) and reverse primers. PCR conditions were as follows: 95 °C for 15 min and 30 cycles of 95 °C—30 s, 58 °C—40 s, 72 °C—60 s, and 72 °C—10 min. PCR products were separated on an ABI 3130xl Genetic Analyzer  and genotyped using GeneMapper 4.0.Muscle tissue samples collected from animals for genetic analyses were stored at −20 °C. DNA was isolated from approximately 30 mg of muscle tissue using the E.Z.N.A Tissue DNA Isolation Kit . A total of 16 autosomal microsatellite loci that were previously used in analyses of Latvian and Estonian wolf populations  were genThe genetic parameters of the population , Nei genetic distance, population differentiation (Fst), mean pairwise relatedness, and number of migrants per generation (Nm)) and PCoA based on pairwise Nei genetic distances were calculated using the standard parameters in the GenAlEx 6.5 program . Indicesn = 506) and eastern (n = 763) parts of the populations , and the genetic diversity parameters were similar (p = 0.997). During this time period, there was a significant tendency for the observed heterozygosity to increase  = 23.47, p = 0.001, R2 = 0.68) and the inbreeding coefficient to decrease accordingly  = 22.22, p =0.001, R2 = 0.67)  of the entire population was 0.732 ± 0.018, the observed heterozygosity (Ho) was 0.713 ± 0.018, and the inbreeding coefficient (F) was 0.026 ± 0.006. The grouping of individuals according to birth year indicated that the groups were not genetically differentiated  .p = 0.611). Comparing the genetic distance between individuals in both regions, some differentiation was observed (p = 0.001). Inbreeding coefficients in both parts of the populations were low: 0.020 ± 0.008 in the east and 0.010 ± 0.008 in the west. Thirteen migrants per generation were estimated between the eastern and western regions. Significant differences in mean pairwise relatedness were found: in the western region, it was higher (0.048 ± 0.001) than in the eastern region (−0.001 ± 0.001).Comparing the eastern and western parts of the populations, the average expected and observed heterozygosity did not differ significantly: 0.737 ± 0.019 and 0.722 ± 0.020 in the east and 0.706 ± 0.018 and 0.700 ± 0.022 in the west, respectively. Allelic diversity was also not significantly different between both parts of the population  = 0.51, observed , but theDuring the study period, 223 groups of related wolves were determined based on full sibling and parent–offspring relationship analyses, incorporating the age, sex, and harvesting date of individuals. The smallest groups consisted of 2 animals, and the largest group consisted of 15 animals. These kin groups included animals from one or more wolf packs and individuals of the same group could be hunted in different hunting seasons. In the majority of groups, related individuals were found only during one or two hunting seasons . Less than a third of the groups existed for four years or longer (29.4%) . The lonWe detected the loss of at least one breeder in 64.6% of the identified kin groups. In 27.1% of the cases, the breeder was hunted while at least one or more pups were still alive. In 17.5% of the cases, one of the breeders was hunted while pups were three to ten months old (5.6 ± 2.0 months old on average). In some packs, we could trace the loss of the breeder and the replacement of the lost individual over the years; two examples are shown in Out of 39 packs in which the loss of one or both of the breeders was detected, the juveniles were hunted during the current hunting season within the same parish in 13 packs and within the boundaries of neighbouring parishes in 17 packs, and the dispersal of juvenile animals beyond 30 km was observed in 9 packs. It should be noted that according to genetic data, in cases where one of the breeders was lost, we did not always know what happened to the other breeder. Perhaps the young animals left the natal territory together with the surviving pack members, but it is also possible that juveniles left the natal territory on their own. In six families, after the loss of one of the parents, the juvenile animals remained with the other parent in the same or neighbouring parish where they were hunted later during the same hunting season. In nine families where the loss of one breeder was confirmed, the juvenile animals were hunted later in the same hunting season 30 to 230 km away, indicating that the juveniles had left their natal territories at a relatively young age.The genetic parameters obtained in this study indicate that, overall, the Latvian wolf population is in a favourable status—inbreeding is low, allelic diversity has not decreased over the years, and genetic diversity is high and similar to that found in Estonian , Polish In earlier years , there wAlthough the western and eastern parts of the Latvian population are not isolated, we observed higher mean pairwise relatedness in the western part of the population compared to the eastern part. This suggests a turnover of individuals between nearby packs in the west, thus ensuring higher relatedness among packs, while in the east, nonrelated animals dispersing further from their natal packs or immigrating from the territories of neighbouring countries might be accepted into packs more often . The relIn groups where a sufficient number of genetic samples were collected, we observed the typical structure of wolf packs—a breeding pair and their offspring of two years ,42. The The loss of a breeder also affects the persistence of the pack and maintenance of the pack’s territory. The fact that in four packs, after the loss of a partner, the other breeder was hunted at a relatively distant location within one to two years suggests that due to hunting disturbance, the surviving breeder left the previous territory and searched for a new partner and territory. We also detected the dispersal of subordinate animals after the loss of the breeders. The territories of wolf packs are quite stable, and as long as one of the breeders and some other animals of the pack have been able to survive, the pack usually maintains its territory . In satuA turnover of individuals has also been found in some naturally regulated, unexploited populations and populations experiencing low hunting pressure ,11,47,48In order to reduce the negative impact of hunting on the stability of the social structure of the population and possible long-term negative consequences, not only the size and distribution of the population should be taken into account when evaluating the annual hunting limit. The impact of hunting on the demographic and social structure of the population, as well as food resources available to predators and the potential future impact of any obstacles to the migration of animals, for example, fences being built on country borders, should also be considered. In addition, when evaluating the duration of the hunting season, the impact of hunting on the social and territorial stability of the population and the possibilities of preserving functional wolf packs should be taken into account.To improve knowledge about social processes in the Latvian wolf population and its kinship structure, we recommend obtaining genetic material from neighbouring countries in order to clarify wolf kinship and social structure in border areas and the importance of immigrants in maintaining the local population. In addition, the introduction of noninvasive genetic monitoring is advisable to obtain more comprehensive information about ongoing processes within and among wolf packs and to provide a more detailed insight into the social dynamics of the population and the dispersal habits of the individuals. Probable effects of social instability on the demographic structure and reproductive processes in the population as well as on the increase in depredation cases should be investigated in future studies.At the time of this study, hunting pressure had not reduced the numbers and distribution of individuals in the Latvian wolf population , negativIt is not expected that the length of the wolf hunting season in Latvia could be changed or the limits reduced in the near future, as the number of wolves has increased in the last decade , society


Ochratoxin A (OTA) contamination is a crucial concern in raisin and currant production. Stochastic and deterministic analysis has been utilized to investigate environmental indicators and drying characteristics. The analysis was performed using two seedless grape varieties (Crimson—red and Thompson—white) that were artificially inoculated with Aspergillus carbonarius during open-air and tunnel drying. Air temperature (T) and relative humidity (RH) were measured and analysed during the drying experiment, along with grape surface temperature (Ts), and water activity (aw). The grape moisture content, fungal colonization, and OTA contamination were estimated, along with the water diffusivity (Deff) and peel resistance (rpeel) to water transfer. Monitoring the surface temperature of grapes is essential in the early detection of fungal growth and OTA contamination. As surface temperature should be carried out continuously, remote sensing protocols, such as infrared sensors, provide the most efficient means to achieve this. Furthermore, data collection and analysis could be conducted through the Internet of Things (IoT), thereby enabling effortless accessibility. The average Ts of the grapes was 6.5% higher in the tunnel than in the open-air drying. The difference between the RH of air and that in the plastic crates was 16.26–17.22%. In terms of CFU/mL, comparison between white and red grapes in the 2020 and 2021 experiments showed that the red grapes exhibited significantly higher values than the white grapes. Specifically, the values for red grapes were 4.3 in 2021 to 3.4 times in 2020 higher compared to the white grapes. On the basis of the conducted analysis, it was concluded that tunnel drying provided some advantages over open-air drying, provided that hygienic and managerial requirements are met.Optimisation of solar drying to reduce fungal growth and  Grapes are one of the most widely grown fruits, with an annual growth rate of almost 75,000 tonnes. Approximately 50% of grapes are utilized in the wine industry: one-third is consumed as fresh, and the remainder is dried. Due to their high moisture content, they are susceptible to spoilage after harvest, even during cold storage. High sugar content and high moisture content stimulate the growth of microorganisms, leading to their spoilage. Drying is an effective method to decrease moisture content to an acceptable level and reduce the risk of microorganism growth. Furthermore, low moisture content decreases the potential for enzymatic degradation and moisture-mediated harmful reactions, making drying an optimal approach for grape preservation. Currently, in various developing countries, the commonly employed grape drying methods are open-air solar drying and drying under shade .Aspergillus section Nigri, Penicillium spp., and others. Fungal invasion depends on grape maturity and quality state. Alternaria, Cladosporium, Botrytis, and Rhizopus are fungi detected at early veraison, whereas Aspergillus and Penicillium are at harvest and during sun drying. Aspergillus carbonarius and A. niger (black Aspergilli) are known to be responsible for Ochratoxin A (OTA) contamination in grapes and raisins   . The Levhrinkage . The bouhrinkage . Inversec, Deff, and surface resistance to water transport (rpeel = 1/kc) for the drying cases tested are seen in The optimisation procedure was based on the input values tabulated in eff was 244% on average (54% in 2020) higher in the tunnel than in the open air. The values in the parenthesis are those from the 2020 experiments conducted by Templalexis et al. [eff between infected and control samples in tunnel drying, infected grapes had more than 50% (70% in 2020) higher Deff than the control grapes. In the case of the open-air drying, the infected grapes had more than 120% (60% in 2020) higher Deff than the control grapes. As can be seen in eff was on average 92% higher in the 2020 experiments compared to the 2021 experiments. In particular, the average temperature was 70% higher in the 2020 experiments compared to the 2021 experiments . This is also explained by the duration of the drying process, which is discussed below. In the light of the previous discussion, and the data in Aspergillus growth is favoured in red grapes compared to white grapes. The grape peel is covered by a waxy layer (cuticle) and has few functional stomata, so water loss is mainly through the waxy cuticle at a relatively slow rate. When the rate of water loss increases due to high temperatures, splitting of the peel occurs. Although macroscopic observations did not agree this phenomenon in both years (2020 and 2021), it cannot be overlooked in the absence of microscopic observations. When the peel is damaged, nutrients are no longer restricted, and the microbial population increases dramatically [peel showed a different response during drying depending on the treatment tested (2021 experiments). In addition, significant differences were found between the 2020 and 2021 experiments. The rpeel was ≈1.4 (2.4 in 2020) times lower for grapes dried in the tunnel than for those dried in open-air  in the open-air drying and 290 h (220 h in 2020) in the tunnel, considering that drying was stopped when no mass loss was observed. In tunnel drying, the rpeel of the control white grapes was 12.8% higher than that of the infected white grapes, and the rpeel of the control red grapes was insignificant compared to that of the infected red grapes. In Aspergillus growth and OTA development on grape peel disintegration. In f(t) along with the variation in OTA with drying time (t) are presented. The grey shaded area is the optimum zone  for A. carbonarius growth. The initial water content of red grapes was higher than that of white grapes in both years (2020 and 2021). The water content of red grapes was 5.89 kgwater/kgdm (4.55 kgwater/kgdm in 2020), and that of white grapes was 4.02 kgwater/kgdm (3.95 kgwater/kgdm in 2020).As can be seen, Ds et al. . This caatically  reportedopen-air . The difand 2021 , but notw in tunnel and open-air drying. This response did not favour aw × temperature induced stress capable of favouring OTA production, as it happened in the 2020 experiments , even with almost four times higher CFU/mL (p ≤ 0.05), although the rate of increase was much higher during the 2020 experiments (p ≤ 0.05) in both years 2020 and 2021 . From this correlation, it was found that the measured difference of grape surface temperatures (Ts|control − Ts|infected) becomes equal to or less than the accuracy of the infrared sensors once the CFU/mL values fall below a threshold. It would be informative to extend this investigation using infrared sensors with varying measurement accuracy. This will enable testing of the minimum CFU/mL levels, at which infrared sensors can detect significant differences between infected and healthy grapes during open-air and tunnel drying. This will be highly significant in developing and applying this method for the early detection of A. carbonarius and OTA production.This study utilises data fusion from three distinct yet interconnected scientific fields such as “Phytopathology”, as OTA accumulation is attributed to A. carbonarius and OTA production in open-air and tunnel drying. Various properties of the surrounding air, drying grapes, and drying properties through Computational Fluid Dynamics simulation (Deff rpeel) were measured to achieve this objective. The analysis of all the collected data revealed that the selection of environmental indicators  for feeding as inputs predictive models of OTA production should be approached with caution. This is due to the significant spatiotemporal variability exhibited by the monitored indicators, which could result in significant bias in OTA modelling. Estimating Deff and rpeel can help in comprehending the mechanisms involved in growth of A. carbonarius and OTA production. These two properties describe the movement of water from the endocarp and mesocarp of grapes to their skin (exocarp). This discussion holds significance, as Deff and rpeel explain the water transport in grapes for the different grape varieties, drying methods, and between healthy and infected grapes.Over the course of two consecutive years (2020 and 2021), this study aimed to identify key measurements necessary for early detection of Crimson seedless) and white (var. Thompson seedless) grapes in the open air and in a tunnel from 08 September 2021 to 23 September 2021 to investigate the role of grape surface monitoring throughout the drying process as well as the environmental conditions and resulting drying properties in A. carbonarius infection and OTA contamination. In order to carry out this study, red and white grapes were taken from a local market and on arrival at the laboratory, defective berries  were discarded, and the remaining berries were divided into two batches: healthy (hereafter referred to as “Control”) and the infected (hereafter referred to as “Infected”) grapes; the latter were artificial inoculated according to the following experimental protocol. The grape berries were arranged in a single layer within shallow, perforated plastic crates (see Twin solar drying experiments were carried out on red (var. ates see  in a spaA. carbonarius (ITEM 5012), previously tested for its ability to produce OTA and kept in the official fungal collection of the Institute of Sciences of Food Production of the National Research Council (ISPA–CNR) in Bari and in the fungal collection of the Department of Sustainable Crop Production (Di.Pro.Ve.S.) of the Università Cattolica del Sacro Cuore in Piacenza, was used for inoculum preparation. The strain was inoculated on Petri dishes containing Potato Dextrose Agar  and incubated for 7 days at 25 °C (12 h light photoperiod). At the end of incubation, the dishes were washed with 10 mL of sterile distilled water, and the suspension obtained was adjusted to a concentration of 104 conidia/mL. Each sample group  was immersed in the conidial suspension for 5 min and allocated for drying. For comparison, untreated grapes  were also included and subjected to solar drying until steady weight was achieved. The grapes were sampled at three sampling times: the beginning of the experiment (7 September 2021), the middle of the experiment (14 September 2021), and the end of the experiment (7 October 2021). At each of the three sampling times, 20 berries were randomly selected in triplicate from the tested clusters, i.e., 3 sampling times (beginning-middle-end) × 2 grape varieties (white-red) × 2 drying treatments (Control–Infected) × 2 drying methods (open air–tunnel drying) × 60 (3 × 20) berries = 1440 berries were analysed in total. After grape sampling, the grapes from different treatments were stored separately in plastic bags at −18 °C until the later analysis of fungal colony forming units (CFU/mL) and quantification of OTA (μg/kg).A strain of −1 to 10−6, plated on Potato Dextrose Agar  amended with cloramphenicol (0.5%), and incubated at 25 °C for 5 days (12 h light photoperiod); the test was carried out in triplicate. The developed black Aspergilli colonies were counted and the results expressed as colony forming units per mL of grape must (CFU/mL). For OTA analysis, the musts were extracted with ethanol (70%) in a 1:1 (v:v) ratio, and the mycotoxin concentrations were determined by Enzyme-Linked Immunosorbent Assay (ELISA). The sample extracts were analysed using AgraQuant Ochratoxin A  for OTA quantification. Mycotoxin extraction and analysis were carried out according to the manufacturer’s instructions and considered reliable [Grapes from the different sampling groups were crushed to obtain must, consisting of juice and berry residue with peel, which was used for both CFU and OTA analysis. The must (1 mL) was added to 9 mL of 1% peptone water, homogenised by vortexing and the suspension was used for serial dilutions from 10During the solar drying experiments, a hygrothermal sensor and data logger, Hobo U10–003 , was used to monitor the air temperature and relative humidity every 10 min, with a resolution of 0.4 °C and 0.5%, and an accuracy of ±0.7 °C and ±3.0% for temperature and relative humidity, respectively. Grape surface temperature was monitored using an array of infrared temperature thermometers Optrics CS LT   with accThe system for collecting and recording the measurements was developed at the Laboratory of Farm Machine Systems of the Agricultural University of Athens, Greece. It consisted of a motherboard responsible for displaying, processing, and storing the measurements and a number of 1 to 8 daughterboards for connecting the sensors. Each daughterboard could read up to 8 analogue sensors with a resolution of 10 bits and a sampling rate of 10 Hz. The daughterboard was based on the ATMEGA32-16PU processor from MICROCHIP (ATMEL). The motherboard was based on ST’s 32F746GDISCOVERY board with STM32F746NGH6 processor and included 1024 KB ROM (Flash), 320 KB RAM, a 4.3-inch touch screen, 4.3-inch SD card, Ethernet, and am RS485 port. The connection between the motherboard and the daughterboard could be wired (RS485) or wireless (Digi XBee 802.15.4 at 2.4 GHz). The software for both boards was written in C and C++. The measurements were recorded on the motherboard’s SD card and in a database (MySQL) on the laboratory’s server. A browser application (WebBrowser) in HTML and JavaScript and an Android mobile application (apk) in Java for Android were developed to monitor the measurements in real time.w was measured using a HygroLab C1  equipped with an HC2–AW sensor at 25 °C. The instrument was calibrated in the range of 0.65–0.95 using SCS certified humidity standards EA65–SCS, EA80–SCS and EA95–SCS. The aw was related to the respective moisture content using the statistical programme Statgraphics 19  and the Guggenheim, Anderson, and de Boer (G.A.B.) sorption isotherm model (Equation (1)). The parameters Mo, C, and Kb were estimated using the Levenberg–Marquardt optimisation method,o is the monolayer moisture content; and C and K are the adsorption constants, related to the interaction energies between the first and the subsequent sorbed molecules at each sorption site. The aw was measured in triplicate on red and white control grapes (tunnel and open air) according to a rotation scheme at the end of each day of the experiment using the “dynamic method”. The average initial moisture content of the grapes was 6.28 ± 0.8 kgwater/kgdm , 3.85 ± 0.2 kgwater/kgdm , 6.84 ± 1.0 kgwater/kgdm , and 3.90 ± 0.2 kgwater/kgdm  and was estimated gravimetrically at the end of the experiments at 105 °C for 24 h [The afor 24 h . Grape dWeibull distribution is a well-known continuous probability distribution used to describe the behaviour of systems or events that have some degree of temporal variability. The Weibull distribution was developed to analyse the strength of materials and the resulting failure with time under stress conditions but has since been used to describe the kinetics of quality control, biology, enzymatic, and chemical degradation. The probability density function of the Weibull distribution is described as follows [The three-parameter  follows .(2)f(x)s place) .Statistical analysis of experimental data was carried out by Statgraphics 19 . ANOVA was used to estimate the average values of CFU/mL, OTA (μg/kg) detected in infected grapes (red and whites), and measured temperatures (air and grape surface). Fisher’s Least Significant Difference (LSD) test was used to identify statistically significant differences between averages. This test is liberal in terms of comparison wise error rate or type I error, compared to other multiple comparison tests , but it is powerful in detecting true differences between averages .


Life on Earth has displayed remarkable adaptability to the harshest environments, spanning polar regions, scorching deserts, abyssal oceans, lightless caves, noxious lakes, boiling hot springs, and nuclear waste sites. These resilient organisms, known as extremophiles or polyextremophiles, owe their survival due to their unique genetic adaptations. This collection, titled ‘Genomics of Extreme Environments’, comprises several articles published in the esteemed journal Scientific Reports. Each article within this collection investigated genetic signature and adaptation in different extreme environments, including the cold polar region, arid desert, oxygen-deprived Tibetan mountains and others. These studies provide invaluable understanding of how life thrives and evolves under extreme conditions, shedding light on genetic mechanisms and adaptation strategies. There are more than a thousand of articles dedicated to tardigrades, highlighting the extensive research conducted on these remarkable organisms. Nevertheless, there is still much more to uncover about extremophilic organisms. Similarly, in the case of penguins, fishes, starfish, flying insects, sheep, and microbes, comprehensive studies emphasize the ample opportunities for further exploration and comprehension of the incredible adaptations exhibited by these diverse life forms in extreme environments.Extremophiles, including polyextremophiles that thrive in multiple extreme conditions, are organisms uniquely equipped to not only survive but thrive in Earth’s most daunting environments, enduring extreme temperatures, intense radiation, high salinity, or extreme pH levels. While much research has focused on extremophilic microorganisms, it is crucial to recognize that animals and insects also possess remarkable adaptations to such hostile surroundings, deserving of further investigation. One noteworthy example is the tiny tardigrade, often referred to as the water bear, which showcases polyextremophilic traits by surviving extreme cold, scorching heat, high atmospheric pressure, radiation exposure, and even the vacuum of space2. In the second article, titled ‘Novel mitochondrial genome rearrangements including duplications and extensive heteroplasmy could underlie temperature adaptations in Antarctic notothenioid fishes’, Minhas et al. presented interesting findings on mitochondrial genome heteroplasmy in Antarctic notothenioid fish3. Utilizing cutting-edge long-read sequencing technologies, the authors assembled and annotated the mitogenomes of five Antarctic notothenioids, revealing the presence of atypical mitogenomes in some species. This study sheds light on the possible role of mitochondrial genome rearrangements and heteroplasmy in the temperature adaptations of these fishes. Moreover, this research team has recently contributed an in-depth analysis of the evolutionary radiation of notothenioid fish, presenting new genome assemblies for 24 species and establishing a time-calibrated phylogeny using comprehensive genome-wide sequence data4. Another intriguing inhabitant of Antarctica, Parochlus steinenii, the only flying insect native to this frigid region, is the subject of the third article5. Through extensive comparative genomic analyses, researchers explore how this insect has adapted to extreme cold temperatures. They identify 68 gene families critical for various biological processes, such as the innate immune system, response to unfolded proteins, protein stability, unsaturated fatty acid metabolism, and DNA packaging. In particular, the expanded acyl-CoA delta-desaturase and Hsc70 gene families exhibited signatures of phylogenetic instability and positive selection after multiple gene duplications, suggesting their possible importance in the adaptation of Parochlus steinenii to cold environments. Together, these articles contributed to our understanding of the genetic mechanisms that allow organisms to thrive in extremely cold environments.Three of the articles in this collection are about living organisms in Antarctica, shedding light on their remarkable adaptations to the extreme conditions of this icy continent. The first article focused on gentoo penguins, investigating their genetic diversity and population dynamics with a particular emphasis on their mitochondrial genome6. In a study conducted by Sun et al., they presented the recovery of five mitochondrial genomes, highlighting genetic differences between deep-sea and shallow-water sea stars7. This study is significant because it provides valuable insights into their phylogenetic relationships and evolutionary history, explaining selection pressure on the mitogenomes of deep-sea starfish and shedding light on the genetic basis of their adaptation. The authors observed a significantly higher A + T content in the mitochondrial DNA of deep-sea starfish compared to their shallow-water counterparts, suggesting genetic distinctions between these two groups.Investigating starfish is of paramount importance, as it elucidates their integral role in upholding the equilibrium of marine ecosystems, uncovers their astonishing biodiversity, delves into their regenerative capabilities, and enables their use as vital indicators of environmental health in our oceans8, an area characterized by low rainfall, heavy sandstorms, sparse vegetation, and a challenging ecological environment. Researchers utilized a combination of transcriptomic and genomic analysis to compare these resilient sheep to normal agro-climatic breeds, examining aspects related to immunity, vision degeneration, and reproduction rates. The study also proposed genetic mechanisms that underlie this adaptation. Additionally, it shed light on the pathways involved in livestock adaptation to extreme desert environments, including ion channel regulation, MAPK signaling, and PI3K-Akt signaling8. In a separate study, another group of researchers published an article titled ‘Comparative analysis of long noncoding RNA and mRNA expression provides insights into adaptation to hypoxia in Tibetan sheep’9. These researchers collected samples from animals living in high-altitude regions characterized by low oxygen levels and a sheep from low altitude. The challenging environmental conditions in these regions make Tibetan sheep an ideal subject for studying genetic adaptation to high-altitude hypoxia. The researchers aimed to uncover the genetic mechanisms involved in the adaptation process by investigating the expression profiles of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in the liver and lung tissues. From the comparative transcriptomic study, the authors identified a set of 247 putative functional candidate genes. Additionally, several positively selected genes within or regulating key pathways were identified for energy metabolism. The findings also have implications for further research on genetic resilience and adaptation in other high-altitude animal populations. Together, the findings from both studies on sheep9 provide valuable molecular markers for adaptability, laying an important foundation for future research and breeding strategies aimed at improving the productivity and resilience of livestock in harsh environments. Furthermore, these studies align with Sustainable Development Goal concepts related to sustainable agriculture, food security, and resilient communities in challenging terrains.In the current collection, two articles focus on sheep research. One of these articles explores the adaptation of indigenous sheep breeds in the southern margin of the Taklimakan Desert13. In an article of this collection, the authors explored the fascinating realm of microbial communities in halite nodules found in Atacama Desert14. They conducted a transplant experiment, relocating halite nodules from a control site in the Atacama Desert to three other sites within the same region. The researchers collected environmental data from each site and conducted a comprehensive analysis of taxonomic and functional changes in the microbial communities, including the exploration of metagenome-assembled genomes, after one year. The study revealed that in sites characterized by extreme dryness, frequent wet/dry cycles, and colder conditions, there was a significant increase in the relative abundance of salt-in strategists, predominantly represented by haloarchaea. These opportunistic microorganisms with salt-tolerant strategies belonged exclusively to the archaea domain and showed site-specific distribution, indicating their adaptation to the new environmental conditions following the transplant.Hyper-arid refers to an extremely arid or extremely dry climate characterized by exceptionally low rainfall. The Atacama Desert, known as one of the driest places on Earth, serves as an example of a hyper-arid region. Numerous researchers have conducted studies related to the diversity, biogeochemical cycles, enzymes, and potential applications of extremophiles and their macromolecules, using samples collected from the extensive desert stretch spanning over 1000 km along northern ChileIn summary, this collection has provided a captivating exploration into the genetic exploration of organisms thriving in harsh conditions. By focusing on diverse environments such as the cold polar region, deep sea, arid desert, oxygen-deprived Tibetan mountains, these studies have significantly advanced our understanding of how life adapts and survives in extreme circumstances. As we look ahead, this field offers exciting opportunities for future research. These genetic insights may find applications in biotechnology and environmental conservation. With ongoing dedication and interdisciplinary collaboration, genomics in extreme environments promises to unlock new horizons.


To assess the putative directionality of these genetic correlations, we conducted a latent causal variable analysis, identifying significant genetic causality proportions (gĉp) linking ALS genetic liability to seven traits. While the genetic component of “self-reported other gastritis including duodenitis" showed a causal effect on ALS , the genetic liability to ALS is potentially causal for multiple traits, also including an effect on "ever being diagnosed with panic attacks”  and inverse effects on “other leisure/social group activities”  and prospective memory result . Our subsequent Mendelian randomization analysis indicated that some of these associations may be due to bidirectional effects. In conclusion, this phenome-wide investigation of ALS polygenic architecture highlights the widespread pleiotropy linking this disorder with several health domains.Leveraging genome-wide association statistics generated from a large study of amyotrophic lateral sclerosis  and UK Biobank , we conducted a phenome-wide analysis of ALS genetic liability and identified 46 genetically correlated traits, such as fluid intelligence score (The online version contains supplementary material available at 10.1007/s00439-023-02525-5. Amyotrophic lateral sclerosis (ALS) is a complex neurological disorder due to the degeneration of the upper and lower motor neurons at the bulbar and spinal level (Rowland and Shneider TAR DNA-Binding Protein (TARDBP), Superoxide Dismutase 1 (SOD1), FUS RNA Binding Protein (FUS), and C9orf72-SMCR8 Complex Subunit (C9orf72)  analysis can provide insights into the biological pathways and/or the causality relationships linking ALS genetic liability to different health domains  investigated 29,612 cases and 122,656 controls, identifying 15 risk loci  available from the UK Biobank (UKB) . This study was conducted using genome-wide association statistics generated by the previous studies. Owing to the use of previously collected, deidentified, aggregated data, this study is exempting from institutional review board approval.https://github.com/Nealelab/UK_Biobank_GWAS. Briefly, the genome-wide association analysis was conducted using regression models available in Hail (available at https://github.com/hail-is/hail) and including the top-20 within-ancestry principal components (PC), sex, age, age2, sex × age, and sex × age2 as covariates. We also analyzed sex-stratified genome-wide association statistics from UKB to investigate possible differences between females and males in the pleiotropy between ALS and other phenotypes. The sex-specific covariates included the top-20 PCs, age, and age2. ALS and UKB genome-wide association statistics were processed by removing variants with a minor allele frequency (MAF) < 1%. With respect to UKB genome-wide association statistics generated from case–control phenotypes, we also removed variants with a minor allele count < 20 in the smaller group between cases and controls. To verify the specificity of the findings identified with respect to ALS, we also analyzed genome-wide association statistics generated from 111,326 clinically diagnosed/'proxy' Alzheimer’s disease cases and 677,663 controls . We decided to use European-ancestry LD scores, because the ALS GWAS was generated from a sample including > 90% individuals of European descent. In line with the recommendations of the LDSC developers  correction .We calculated the SNP-based heritability for ALS using genome-wide data available from a previous study .To verify the transferability of genetic effects between the ALS GWAS meta-analysis  based on the 1000 Genomes Project Phase 3 reference panel for European populations. The clumping procedure was conducted considering a p value of 10–5 for the exposure GWAS. Similar to previous MR studies . For the 4,024 traits available from UKB, we estimated the SNP-based heritability and considered 957 phenotypes with an SNP-based heritability z score > 4  for further analyses as recommended by the LDSC developers , body mass index , forced expiratory volume in one second , and hand grip strength . Since UKB has a prevalence similar to the one observed in the UK population , there are only a limited number of ALS cases in the UKB (N = 167). Accordingly, ALS assessed in UKB (G6_ALS: Motor neuron disease) did not show a statistically significant SNP-h2 heritability and was not tested in the genetic-correlation analysis. Nevertheless, we demonstrated that a PRS derived from the ALS GWAS meta-analysis . Considering the 957 phenotypes with an SNP-based heritability z score > 4, ALS genetic-correlation analysis identified evidence of genetic overlap surviving multiple testing correction for 46 of them  were observed in the rg estimates of these traits calculated using sex-stratified UKB genome-wide association statistics . Ten of the ALS significant genetic correlations were related to traits derived from cognitive function tests. Specifically, ALS genetic liability was negatively correlated with fluid intelligence score  and items related to its assessment . Additionally, ALS genetic liability was positively correlated with two items related to the prospective memory test  and one related to the pair-matching test . Four ALS genetic correlations were related to leisure and social activities. Specifically, ALS genetic liability was positively correlated with spending time in pub or social club , but it was inversely correlated with being part of religious group , attending adult education classes , and other group activities . We also identified two FDR-significant genetic correlations related to non-work-related transportation choices where ALS genetic liability was negatively correlated to walking  and using public transportation . With respect to education, ALS genetic liability was positively correlated with not having any qualification  and negatively genetically correlated with having college education , secondary education , and other professional qualifications . As also mentioned in the original ALS GWAS , “ever contemplated self-harm” , and “ever diagnosed with panic attacks” . With respect to physical health, we identified genetic correlations related to self-reported other gastritis including duodenitis, rg = 0.28, p = 1.4 × 10–3). Finally, we also observed that ALS genetic liability was negatively correlated to items related to participation in the UKB dietary questionnaire: acceptance of the invitation to complete online 24-h recall dietary questionnaire  and number of diet questionnaires completed .We also observed that ALS genetic liability was correlated with multiple mental health outcomes such as "ever thought that life was not worth living” , we identified putative causal effects linking ALS genetic liability to seven of the genetically correlated traits  were observed in the gcp estimates when testing sex-stratified UKB data . Among the traits identified in the sex-combined LCV analysis, only in one instance, the genetic liability of a trait  showed a positive causal effect on ALS . The other LCV results were related to putative causal effects of the genetic liability to ALS on other traits. Three of these showed positive effects of ALS genetic liability (LCV rho > 0): ever being diagnosed with panic attacks , duration screen displayed in the prospective memory test , and number of attempts in the prospective memory test . Conversely, other three traits appear to be negatively affected by ALS genetic liability (LCV rho < 0): number of diet questionnaires completed , other leisure/social group activities , and prospective memory result .To test causal effects and shared genetic mechanisms underlying ALS genetically correlated traits, we performed a genetically informed causal inference analysis using the LCV approach (O'Connor and Price q < 0.05), we identified significant genetic correlations among 18 phenotype pairs . In line with the fact that they are part of the same construct, the strongest genetic correlation (|rg|> 0.89) was observed among items related to prospective memory tests . The prospective-memory items showed a strong genetic overlap with the number of diet questionnaires completed  and leisure/social group activities . Another strong genetic correlation was observed between panic attacks and “other gastritis including duodenitis” . The remaining phenotype pairs showed a lower extent of genetic correlation (∣rg∣ < 0.4). Additionally, we also verified the specificity of the LCV-identified traits for ALS with respect to AD. Although there is a partial shared genetic correlation between ALS and AD , the only LCV-identified trait for ALS that is also genetically correlated with AD at a nominal significance level was “Duration screen displayed” during the prospective memory test .To understand better the pleiotropy linking the traits identified by the LCV analyses, we performed a genetic-correlation analysis among them. After multiple testing correction . However, we did not identify an effect in the opposite direction  or bias due to horizontal pleiotropy .Finally, to validate the causal effects identified using the LCV approach, we performed a two-sample bidirectional MR analysis . LCV and MR are based on different assumptions . Additionally, the genetic liability to ALS appears to have a possible causal effect on participating in leisure group activities. This observation is in line with previous evidence reporting that cognitively intact ALS patients manifest impairments in social skills, manifesting as an inability to recognize others' emotions and a change in behavior toward apathy . This less informative phenotype was based on a single ICD code rather than a phecode combining information from multiple ICD codes. Although this putative causal effect is supported only by the LCV method and not the MR analysis, we speculate that this may be related to the interplay among gut microbiota and the enteric neuro-immune system as a pathogenic path to ALS and other neurodegenerative diseases ]. Other gastritis phenotypes were also available in UKB, but for most of them, the number of cases was much lower than the one observed for gastritis–duodenitis phecode . Another gastritis–duodenitis UKB phenotype  with a similar number of cases is available and it showed a genetic correlation with ALS that was just outside the significance threshold defined for multiple testing correction  contributed to the understanding of the possible directionality linking ALS genetic liability to cognitive function, (ii) highlighted the need for further studies to assess the impact of alcohol consumption and dietary habits on ALS risk, and (iii) opened a new possible direction in the study of ALS genetics.Supplementary file1 (TIFF 1024 KB)Supplementary file2 (XLSX 223 KB)Supplementary file3 (XLSX 81 KB)Supplementary file4 (XLSX 14 KB)Supplementary file5 (XLSX 63 KB)Supplementary file6 (XLSX 18 KB)Supplementary file7 (XLSX 12 KB)Supplementary file8 (XLSX 14 KB)Supplementary file9 (XLSX 13 KB)Supplementary file10 (XLSX 10 KB)Supplementary file11 (XLSX 21 KB)Below is the link to the electronic supplementary material.


A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation  alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated). Plant breeders have 2 main objectives—derive high-performing varieties at each cycle and improve the mean genetic value of their germplasm so as to be able to generate superior varieties in future generations. The mating design, i.e. the choice of the set of parental lines to cross and their combination, as well as the progeny size per cross, is critical to ensure both short- and long-term genetic gains. However, the number of candidate crosses is putatively very high while the number of crosses and progenies that can be experimentally tested is often limited.cross selection criteria (CSC) that estimate their ability to produce superior progenies for a given trait of interest. The simplest way to rank crosses is based on the expected mean genetic value of the progeny that can be estimated by the mean additive genetic value of the parental lines, the so-called parental mean (PM) criterion ] was developed to estimate genetic values from genotypes [genomic estimated breeding value (GEBV)]. GS uses a training population (TP), which is phenotyped and genotyped to estimate the effects of segregating genomic variants (markers). Assuming that marker effects are additive, the GEBV of an individual is the sum of its allele effects at every marker. According to several simulation studies , compareGenomic predictions offer a promising alternative to estimate progeny variance using marker effects and recombination rate estimates. The progeny distribution can be estimated by simulating progeny in silico (stochastic simulation), placing recombination of parental genomes along chromosome sequences according to a recombination map , with UC = PM + i*h*σ, where i is the selection intensity corresponding to the fraction q of selected progenies, h is the square root of heritability, and σ is the progeny variance in our context. Note that when using UC in a GS context, h2 (and thus h) is usually set at 1 for GEBV, but further research would be required to be sure that this assumption has no influence on the results. As an alternative to UC, Another strategy is to predict the average genetic value of a superior fraction of the progeny of candidate crosses. σ)—and the variance of progeny means—var(PM)—in the list of candidate crosses. When var(PM) among crosses is highly superior to var(σ), PM alone is enough to predict the rank of crosses.Several studies compared the efficiency of those CSC in short-term selection responses (one generation)  lines from the French National Research Institute for Agriculture, Food and Environment (INRAE)-Agri-Obtentions (AO) winter bread wheat breeding program. We tested several previously published CSC  and adapted 2 new ones from the literature that had never been tested per se. From 8–F9 winter-type bread wheat lines developed and phenotyped between 2000 and 2016 by breeders from the INRAE and its subsidiary breeding company AO  Unselected population: we considered that the parental population composed of 835 historical breeding lines from INRAE-AO had never been selected for the trait of interest. QTL positions and effects were randomly assigned.(1b) Selected population: an ancestral population created as in (1a) was further crossed and selected via 3 in silico cycles to produce the parental population. Note that the genetic architecture of this population was the same as the corresponding Unselected parental population, i.e. QTL and their effects were simulated in the starting Unselected population.The accuracy of marker effect estimates.TRUE: QTL effects and positions were supposedly known (or perfectly estimated). The TRUE scenario provides information about the maximum potential of CSC if the TP is optimal and marker effects are perfectly estimated. If the relative performances of CSC in this ideal scenario are not convincing, there is no use implementing them in breeding programs.(2a) ESTIMATED: marker effects were estimated by GS using parental simulated phenotypes and removing QTLs in genotypes ((2b) enotypes . We testThe constraints to maintain genetic diversity in the breeding material.CONSTRAINT on parental contributions and genetic distance between parents .(3a) NO CONSTRAINT .(3b) We simulated the 8 scenarios that are summarized in 2. TBV were calculated as the cross product between QTL effects and allelic doses.The parental population was built with genotypes of the 835 historical breeding lines from the INRAE-AO breeding program. In order to take into account the uncertainty in the genetic determinism of quantitative traits, we simulated 30 random genetic architectures controlled by 300 QTLs randomly picked among the 16k SNPs, with normally distributed genetic effects variance , historiPopulations under selection for 1 or several traits of interest present negative covariances between QTLs. This phenomenon is called the Bulmer effect . Hence, 2 to their TBV Phenotypes of unselected parents were simulated with a heritability Marker effects were estimated by backsolving the model using the PostGSf90 software package For the ESTIMATED scenarios, we used a GBLUP model to estimate parental line genetic values and marker effects according to the following model:i denotes the name of the parental line (n = 835), Y is the vector of phenotypes, μ is the average phenotype, α is the vector of genetic values, and e is the vector of residual effects. The genetic values were assumed to follow G(1) is the genomic relationship matrix computed as Z being the centered genotyping matrix, excluding QTL genotype, and l, and where where  package .The expected variance of progeny was computed using the formula provided by β is either QTL effects for TRUE scenarios (length β = 300) or estimated marker effects for ESTIMATED scenarios ; l for parents l and k for parents l or k or 0.25 if alleles are in coupling phase  or 0.25 if the alleles are in repulsion phase]; and l and k. The recombination rates were computed from the Western European recombination map published by k and l (where  k and l .n = 120) was highly time consuming. We accelerated this estimation as described in The estimation of progeny variance for a high number of crosses  and of simulations  will determine the value of the objective function to maximize  but are also subject to constraints (e.g. the number of progeny per cross and per parent could be limited). When the equation system is linear for the variables to adjust, linear programming may be used to find the set(s) of variables that maximize the objective function. Otherwise, for more complex problems, heuristic algorithms such as genetic algorithms (GAs) may be used to obtain a good (but not necessarily the best) problem solution.A mating design was defined by a vector giving the number of progenies D = 3,300.C1: the total number of progenies was set at C2: the number of progenies allocated to a cross ranged from C3: the number of crosses ranged from C4: the number of progenies derived from 1 parent could not exceed C5: the number of recruited parents for the mating design ranged from G(2) in which SNPs were weighted according to local LD in order to take into account the very heterogenous LD in bread wheat, which markedly increases from telomeres to centromeres. This variance–covariance matrix was computed as WW’, where W was obtained by centering and scaling each column of the genotyping matrix Z such that l and C6: highly related parental lines could not be crossed. We used the LDAK software package , 2 scenarios with different genomic prediction accuracies (TRUE or ESTIMATED), and 2 scenarios with different diversity constraints applied on the mating designs (CONSTRAINT and NO CONSTRAINT).One mating design is defined by a set of crosses and their respective number of progenies. For each CSC, the mating design maximizes a specific objective function under constraints C1–C6 for the CONSTRAINT scenarios and C1 and C2 for the NO CONSTRAINT scenarios.The usefulness of the α is either the TBV of parents for TRUE scenarios or GEBV for ESTIMATED scenarios. The objective function to maximize is the following:where q = 7% best progeny of a cross, computed as follows . An approximation of with ollowing :f is the density function of a Gaussian law N and N = 1, M = where The objective function to maximize is the following:λ, as suggested by λ, we use the genetic value (TBV for TRUE scenarios or GEBV for ESTIMATED scenarios) of the best parental line. The probability of a λ is λ is λ is λ is equivalent to minimizing the objective function:This criterion ranks crosses based on their ability to produce a progeny exceeding a threshold q (e.g. q = 7%) of progenies of the whole mating design, where q is the usual proportion of selected progenies. The same selection threshold q of the progeny genetic value distribution. The expected proportion of progeny of genetic value superior to q:This criterion aims to maximize the expected mean of the superior quantile q best progenies is thus equal to the following:The best mating design is obtained by maximizing this objective function, with the constraint b, the effects of haplotypes carried by parents The objective function to maximize is the following:In the CONSTRAINT scenarios, for all CSC but EMBV and UC3, the objective function and constraints constituted a system of linear equations. We used an integer linear programming algorithm implemented in IBM ILOG CPLEX software  of a cross actually depended on the number of progenies allocated to the cross. To optimize mating designs for EMBV and UC3 criteria, we used a GA. GAs are population-based metaheuristics inspired by Darwinism . The GA For all criteria, we tested whether the preselection of candidate crosses with the highest PM would influence the value of the objective function to be maximized . For allD = 3,300 progenies (constraint C1).In the NO CONSTRAINT scenarios, we did not use optimization software to optimize mating designs, except for UC3. For all other CSC, crosses were ranked based on CSC values, and the 55 best crosses received The F5 RIL progenies of each mating design were simulated using the MOBPS R package  of a mating design optimized using CSC for the genetic architecture p (P = 30 different genetic architectures) for scenario s. The progeny selection rate (K/D) ranged from 1/3,300 (the very best progeny) to 10%. The term TBV stands for the mean TBV of all candidate parents.where Genetic diversity within progenies depends on the diversity of the selected parents and the progeny distribution across the selected parents and crosses.p in scenario s and l in population p . The relative change in genic variance in the K/D selected progeny obtained using a mating design optimized for CSC compared to PM in scenario s was calculated as follows:To compare mating designs optimized according different CSC in terms of diversity management, we used 2 statistics, 1 considering the whole genome that calculates the genetic similarity between recruited parents  and 1 comth progeny simulation for architecture p. To evaluate the ability of CSC to improve the new breeding population in terms of both gain and diversity, we set a 7% selection rate, corresponding to a realistic selection rate at the F5 stage in a bread wheat breeding program, and computed the relative increase in the mean progeny TBV and the relative increase in the progeny genic variance.where D progenies), and OHV [best theoretical progeny value ]. They were computed with TRUE or ESTIMATED marker effects and using parents from Unselected or Selected populations for 30 different trait architectures.Crosses were selected using 7 genomic CSC, namely PM , UC1 , UC2 , UC3 , PROBA , EMBV  to 10%.For TRUE scenarios, all criteria were superior to PM when selection was strong (selection rate < 1%). For example, for all CSC but OHV, the mean TBV of the selected progeny increased by around 4% for Unselected scenarios and by up to 5% for Selected scenarios compared to PM. For ESTIMATED scenarios, the relative increase barely exceeded 1% for all scenarios.t-tests computed within each of the 4 scenarios identified 3 significant groups (P < 5% after Bonferroni correction) for TRUE scenarios: the upper group consisted of UC1, UC2, UC3, PROBA, and EMBV; the middle group consisted of PM; and the lower group consisted of OHV. The pairwise t-tests were not significant for the ESTIMATED scenarios, except for OHV, which was significantly lower than the other CSC. In conclusion, CSC alternatives to PM (except OHV) were superior to PM only for TRUE scenarios, with no substantial differences between them.The ranking of criteria to maximize the TBV of selected progeny changed slightly with the scenario and selection rate. For TRUE + Unselected scenarios, the best criterion to maximize the value of the best progeny was UC3, with a 4.1% average increase in the TBV of the best progeny and a 1.9% SD; for TRUE + Selected scenarios, the best criterion was EMBV (5.2% ± 1.7%); for ESTIMATED + Unselected scenarios, the best criterion was PROBA (1.1% ± 3.1%); and for ESTIMATED + Selected scenarios, the best criterion was UC2 (0.9% ± 2.9%). Pairwise Note that PROBA and UC3 slightly underperformed for TRUE + Selected scenarios when selection was strong (low selection rate). Other CSC such as UC1 or UC2 should be preferred in that case. For all scenarios, the OHV criterion provided the lowest genetic gain. It was very disadvantageous compared to PM for all scenarios at >1% selection rate.In conclusion, when QTL effects are perfectly estimated (TRUE scenarios), CSC based on progeny variance estimation  could increase the genetic gain by up to 5% in breeding programs.For example, for TRUE + Selected scenarios , and their contribution to the mating plan, in terms of number of progenies and crosses, depending on CSC . LookingIn CONSTRAINT scenarios, the contribution of the best parent to the progeny was around 4% for all CSC. At the opposite, in NO CONSTRAINT scenarios, the contribution of the best parent reached up to 25%.OHV and PM are the 2 opposite extremes for all scenarios in terms of parental contribution, with OHV allocating less progenies to the top parents and UC2 intermediate. In CONSTRAINT, Selected, and TRUE scenarios, progenies were allocated to a more diverse set of parents compared to NO CONSTRAINT, Unselected, and ESTIMATED scenarios. The maximum is 25% of progenies attributed to the best parent for NO CONSTRAINT TRUE Selected scenarios using PM and PROBA, and the minimum is 7% for OHV. UC2 is in between, with 18% (14%) for Unselected (Selected) TRUE scenarios and 61% (55%) for ESTIMATED scenarios. A majority of progenies is attributed to the top 20 parents. For example, in CONSTRAINT scenarios, the top 20 parents produced 76% of progeny using criterion PM, 69–72% of progeny using PROBA, UC3, or EMBV, 51% using UC2, and 40% using OHV .Looking at the distribution of contributions to crosses, it is not PM but UC3 and PROBA that allocate the highest number of progenies to top crosses. OHV select more crosses resulting in higher genetic diversity. UC2 is intermediate.The constraints that we used  increased the genic variance by 10% (8–15% depending on the CSC and scenario) in the new breeding population and reduced the mean genetic value by 5% 4–8%; . IncidenThe CONSTRAINT scenarios had a significant negative effect on the TBV of the 7% progenies and the best progeny and positive effect on genic variance in top progenies, especially when using PM. This could be explained by the fact that the selected crosses using CONSTRAINT are suboptimal compared to NO CONSTRAINT in terms of genetic gain by forcing a minimum level of diversity in parents. For NO CONSTRAINT scenarios, the algorithm assigns a maximum number of progenies (60) to the 55 best crosses, while for CONSTRAINT scenarios, the objective function maximizes the sum of CSC of all selected crosses, with a limit of 250 progenies per parent for the whole design. Although selected parents were stable between different runs of a same scenario, the mate allocation seemed random with CONSTRAINT using PM or EMBV compared to other CSC ; for the TRUE + Selected scenarios, UC2 provided the best progeny (2.9% ± 2.4); for the ESTIMATED + Unselected scenarios, PROBA was the best criterion (0.7% ± 2.3); and for the ESTIMATED + Selected scenarios (closer to breeding programs), UC3 was the best criterion (1.3% ± 1.9).N progenies), and OHV . One goal of the present study was to rank CSC based on their ability to provide superior short-term genetic gain but also to assess their impact on genetic diversity management. With perfect marker effect estimation (TRUE scenarios), mating designs optimized using progeny variance estimates using UC1, UC2, or EMBV provided superior genetic gain and diversity in top progeny than mating designs solely optimized with regard to parental breeding values. With estimated marker effects (ESTIMATED scenarios), UC1, UC2, and EMBV maintain a level of genetic diversity significantly superior while maintaining genetic gain.Several CSC have been proposed to rank the crosses that are focused on different properties of the right-hand tail of the predicted distribution of progeny breeding values: UC , PROBA , EMBV  provided superior genetic gain in top progeny when selection was stringent. The OHV criterion was associated with a minor genic variance loss but also the lowest genetic gain. The potential of OHV to maintain genetic diversity had already been demonstrated by This study tested the interest of 2 CSC, PROBA and UC3, adapted from recent literature. The PROBA criterion, as described by The UC3 criterion aims to maximize the expected value of the 7% best progenies of the whole mating design. It is a direct application of the index 5 criterion concept tested in In conclusion, according to the pareto front in The major issue in genomic predictions is the precision of the estimation of marker effects, which depends on the size of the TP and its relevance of the TP (relatedness between the training and the prediction sets), the accuracy of phenotypes , and the genomic prediction models. The TRUE scenario provides information about the maximum potential of CSC if the TP is optimal and marker effects are perfectly estimated. Only if the relative performances of CSC in this ideal scenario are convincing, it is worth testing it in more realistic simulated breeding programs. This is a reference value that tells us how optimal is our design, on which parameters we can work to improve our predictions and the maximum gain we can ever get.According to σ) was on average 4–22 points lower than the correlation between the estimated  with an average −0.82 ± 0.04 bias and 0.41 ± 0.07 correlation. Another strategy for estimating marker effects is to use selection models such as Bayesian Lasso that basically remove markers having very minor effects. In The lower accuracy of progeny variance estimates compared to genetic values has been reported in many studies (t = var(σ)/var(PM). To understand why, let us follow the reasoning of q of the progeny of a cross is computed as UC = PM + i*σ, with i being the selection intensity corresponding to the selected quantile q. The variance of UC values is thus equal to var(PM) + i2*var(σ) + 2*i*cov. We can thus hypothesize that the lower the t ratio, the more the UC variance could be explained by the PM variance. In other words, when the t ratio is low, the genetic values of parents (e.g. ∼PM) drive the expected superior progeny value. Hence, all CSC tend to select the same crosses, leading to a low additional genetic gain of alternative CSC over PM. For ESTIMATED scenarios (t = 2–3%), the t ratio was 4-fold lower than for TRUE scenarios (t = 6–11%) .The benefits of CSC based on progeny variance estimation also highly depend on the ratio between the progeny SD and progeny mean variance = 6–11%) . As expet ratios, and populations with a high t ratio systematically showed higher alternative CSC benefits. We can list some population types that are expected to have a high t ratio and would thus be worthy of CSC implementation. First, selection can increase t ratio. In σ) but at a lower extent (1.4-fold lower for Selected compared to Unselected scenarios). The TRUE t ratio thus increased, along with the expected benefits of CSC based on progeny variance estimation. Second, structured populations can also lead to high t ratios. Structured populations arise when crossing elites with genetic resources (GR) from different genetic groups, in prebreeding programs for instance, or to a lesser extent when crossing elite parents to elites from different breeding companies. When crossing parents from 2 highly differentiated populations, the t ratio may increase because of a higher magnitude of var(σ). Genetic differentiation leads to higher polymorphism between parents from different genetic groups (Wahlund effect) and among progenies and thus higher progeny variance. According to σ reported in several publications in maize . The ratio obtained in our study using elite bread wheat material ranged from 2 to 11%  and the genetic similarity of parent Φ. As a rule of thumb, the overuse of the few (best) parents . In practice, the breeders may not allow progeny size to vary widely among crosses as this is the case in our simulations. But they can decide to have regular size progenies for most crosses and extended sizes for a few crosses with high UC1, UC2, or PROBA. It would be interesting to add to the pipeline the estimation of the size of the progeny to assure the achievement of a realistic value for PROBA, especially when the possibility to include several traits will be included.Some more work is also necessary to optimize the threshold we choose for PROBA. It must be in the range of variation of the putative parents in order to be realistic. Putting an extreme threshold on PROBA would be equivalent on focusing on the extreme tails of the progeny distribution. When the threshold is too high (no cross can provide progeny that satisfies the constraints), all crosses have a PROBA close to 0. When the threshold is too low, all crosses provide a PROBA close to 1. So, the risk choosing extreme thresholds is that PROBA will not be discriminant or the probability to actually sample such extreme progeny in selected crosses would be too low with our constraints on progeny size. In our case, setting the threshold as the value of the best line in the TP gave a discriminant PROBA.Regarding computation time, we provide a solution to quickly estimate progeny variance in t ratio var(σ)/var(PM) among crosses increases and should be evaluated in a prebreeding program.For an elite winter bread wheat breeding program, crossing parents with the highest genetic values is likely not the best means to maximize the usefulness of progeny. Alternative CSC that take progeny variance estimation into account could provide better elite progeny and improve the mean population value while maintaining more genetic diversity. UC1 is a good compromise for short-term genetic gain with limited loss of genetic diversity, and UC2 should be prefered for maintenance of genetic diversity that could be of interest for longer-term genetic gain, in a prebreeding context for instance. However, the efficiency of these alternative CSC depends on the progeny variance estimation accuracy, which requires some improvement. The size and quality of the TP should be increased as well as marker effect estimates. Moreover, the interest of CSC compared to mean parental value increases when the jkad195_Supplementary_DataClick here for additional data file.