For all affected individuals, we calculated their mean standardized FGRS for each disorder. The patterns of FGRS were quite similar for MD and AD, and for AUD and DUD, but substantially less similar for BUL and AN, BD and SZ, and ADHD and ASD. While OCD had high levels of FGRS for MD and AD, the overall FGRS profile differed considerably from MD and AD. ADHD FGRS scores were substantially elevated in AUD and DUD. FGRS scores for BD, OCD, AN, ASD, ADHD, and especially SZ were relatively disorder-specific while genetic risk for MD and AD had more generalized effects. The levels of FGRS for BMI, coronary artery disease, and educational attainment across our disorders replicated prior associations found using molecular genetic methods. All diagnostic categories examined had elevated FGRS for many disorders producing, for each condition, an informative FGRS profile. Using a novel method which approximates, from pedigree data, aggregate genetic risk, we have replicated and extended prior insights into the structure of genetic risk factors for key psychiatric illnesses.To clarify the structure of genetic risks for 11 major psychiatric disorders, we calculated, from morbidity risks for disorders in 1st–5th degree relatives controlling for cohabitation effects, in the Swedish population born between 1932 and 1995 ( These studies have been quite informative but have some limitations. For twin studies, sample sizes have not been insufficient to study key rarer disorders such as schizophrenia and autism spectrum disorder. For molecular genetic studies, case–control samples have been typically assembled from a wide variety of countries, using a range of ascertainment strategies and diagnostic approaches. Furthermore, few approaches to date have utilized a different analytic approach—calculating the average magnitude of genetic risk for each proband group for a wide array of disorders.Several different approaches have been taken to clarify the structure of the genetic risk factors for major psychiatric disorders (including substance use disorders). Most commonly, structural equation modeling (SEM) has been applied to twin samplesn = 5,830,014): major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism spectrum disorder (ASD). Family genetic risk scores (FGRS) are calculated for each disorder from the weighted rates of that disorder in 1st through 5th degree relatives, controlling for cohabitation effects. We added Family in this term to clearly differentiate this statistic from PRS, as the FGRS derives its information not from molecular variants but from the phenotypes of a proband’s family. This method is complimentary to PRS for the analysis of psychiatric disorders. Its current main advantage is its availability for the entire population of Sweden, coupled with excellent medical and other national registry data. Its advantage over twin and twin-family analyses conducted by structural modeling is its use of a far wider range of relatives thereby providing substantially greater information about genetic risk.In this report, we address some of these prior limitations by examining individuals with a lifetime diagnosis of 11 psychiatric disorders ascertained through national registries in the Swedish population born between 1932 and 1995 , coronary artery disease (CAD), and years of education (YOE).We collected information on individuals from Swedish population-based registers with national coverage linking each person’s unique personal identification number which, for confidentiality, was replaced with a serial number by Statistics Sweden. This study was approved by the Regional Ethical Review Board of Lund . Our database consisted of all individuals born in Sweden between 1932 and 1995 ofz-score for relatives with the trait and the mean z-score for individuals without. For 1st degree relatives we also multiplied the z-score with a factor that sought to correct for the influence of shared environmental factors separately for siblings and parent–offspring pairs. For parent–offspring pairs, this correction was implemented by comparing the resemblance, by logistic regression, for father–offspring pairs where the father sired and raised his child  to the resemblance between children and their not-lived-with fathers, i.e., those who sired their offspring but never lived with or near them when they were growing up. We have examined such not-lived-with fathers in several prior extended adoption studies7 as reflecting parent–offspring resemblance resulting only from genetic effects, analogous to the biological parent in an adoption design. For sibling pairs, we compared the resemblance in half-sibs who were versus were not reared together. Having the same genetic relationship between these siblings, this comparison also isolates the genetic from the shared environmental effects. As seen in Fig. parents who themselves were born in Sweden and followed through Dec 31, 2017. We added the later requirement to ensure roughly comparable numbers of relatives for each of our probands. In the database, we included date of registration for major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism spectrum disorder (ASD), utilizing ICD-8, 9, 10 codes from Swedish national primary care, specialist and hospital registries as well as information from Prescription and Criminal registers for AUD and DUD  is much more modest than are seen with the MD–AD and AUD–DUD pairs.Figure Second, the findings displayed by FGRS in Fig. Given the novelty of this method, our final analyses attempt to validate our FGRS method by examining, across our 11 disorders, the FGRS scores for BMI, CAD, and YOE Fig. . For BMIFirst, a number of prior approaches, using latent variables applied to genetic correlations, have examined the structure of genetic risks for major psychiatric disorders5. Using a different method, our results also provide nuanced evidence for the inter-relationships of the genetic risks for different disorders. The two most striking examples are the close relationships revealed between MD and AD, and between AUD and DUD.Of the many points of possible interest in these results, we review four. 5, the relationship is evidenced both by a reciprocal relationship between their FGRS scores (AD FGRS is the second highest score for MD patients and MD FGRS is the second highest FGRS for AD patients), and by a quite similar profile of their other FGRS scores. It is instructive to compare their two profiles with that of OCD which, when included in phenotypic analyses, typically aligns in factor analyses with other internalizing disorders10 but in a recent molecular genetic multivariate analysis it was shown to cluster instead with AN and Tourette’s syndrome4. With AD and MD, OCD shares high levels of AD and MD FGRS, but the pattern of other FGRS are quite different including, consistent with prior studies11, elevated levels of genetic risk for AN.For MD and AD, consistent with prior evidence for a close genetic association between these two “internalizing disorders”5, the strong relationship between AUD and DUD is marked by sharing the same two highest FGRS for AUD and DUD. But differences in the FGRS profiles of these two drug use disorders are also evident. In particular, DUD, compared to AUD, has considerably higher levels of FGRS for MD, AD, BD, SZ, and ADHD.Congruent with prior evidence for shared genetic underpinnings for externalizing disorders13. However, the FGRS loadings for AN in BUL cases, and BUL in AN cases, were rather modest. Their genetic profiles also differed in other ways. For example, consistent with prior studies, BUL had a substantially higher FGRS scores for the externalizing disorders of AUD, DUD, and ADHD than did AN15.However, our findings did not confirm close genetic relationships between two other pairs of disorders expected to be closely genetically related: AN and BUL, and BD and SZ. Family and twin studies show substantial sharing of genetic risk between AN and BUL17, but are with earlier family studies20 showing quite modest co-aggregation. While the FGRS score of BD is the second highest observed for SZ, it is much weaker, and the association is not reciprocal as the SZ FGRS is the eighth strongest FGRS for BD. Furthermore, the profile for other FGRS scores differs meaningful across the two disorders.Our results are also not consistent with recent reports of high genetic correlations between SZ and BD21 as the ASD FGRS is the second strongest for SZ and the SZ FGRS is the third strongest for ASD. Also, consistent with prior molecular genetics’ findings5, our two neurodevelopmental disorders have a reciprocal genetic inter-relationship as the ADHD FGRS is the second strongest for ASD, and ASD FGRS the third strongest for ADHD.Our results do provide support for a modest reciprocal genetic relationship between SZ and ASD reported elsewhereSecond, the findings as presented in Fig. 22 have a genetic substrate somewhat different from the other disorders examined. They substantially predispose to two disorders that are common, often disabling and are leading causes of world-wide disability23. However, these genetic risk factors also make important contributions to a wider range of disorders including BD, ADHD, and substance use and eating disorders.Third, given the novelty of our FGRS methods, we sought to validate them in three non-psychiatric phenotypes to see if we could replicate previously observed genetic associations. We examined BMI because of intriguing evidence that genetic risk for AN was associated with constitutional thinness25. We replicated that finding showing a stronger inverse association of AN FGRS with BMI than any of the other disorders. Consistent with prior reports, we also found a negative association between our FGRS for BMI and FGRS for SZ26 and BD25, and a positive association between the BMI FGRS and ADHD25. We also showed, for the first time to our knowledge, a positive correlation between the FGRS for BMI and BN, consistent with the finding that a BMI polygene risk score predicted binge eating and purging in a general population sample27.29. We also replicated those results. Finally, previous studies have found a positive genetic correlation between the genetic predisposition to high educational attainment and risk for ASD, BD, and AN31 and a negative genetic correlation with AUD32 and DUD33. We could reproduce all these associations. The ability of FGRS to consistently replicate a range of previously reported genetic correlations supports the validity of this method.Prior genetic epidemiological and molecular genetic data have found an association between risk for MD and risk for CADFourth, perhaps the greatest contribution of these analyses to insightful prior work on the genetic relationships between disorders has been the value of examining, for our patient cohorts, not only their genetic risk for the disorder from which they suffer, but also the pattern of their genetic risks for multiple other disorders. Our results suggest that examining such genetic profiles, we can gain a richer understanding of the genetic substrate for our disorders and the genetic relationship across disorders.37. The validity of MD diagnoses is supported by its prevalence, sex ratio, sibling and twin correlations, and associated psychosocial risk factors39. Genetic epidemiological findings for AUD, DUD, and eating disorders in Sweden have been similar to those found in other samples42. We are unaware of attempts to validate Swedish diagnoses for ADHD, AN, ASD, or BUL.These findings should be viewed in the context of eight potential methodological limitations. First, the validity of the FGRS score is dependent on the quality of the available diagnoses in the Swedish national registries which has been well demonstrated for SZ, BD, and OCDSecond, ASD had a usually low male-to-female ratio of 1.33:1. We therefore examined this ratio as a function of age at first registration , is of greater value than replicating findings using the same methodsSupplemental material.


Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions.An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time.The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures. Graphs are a well-known mathematical structure used to encode relationships among elements of a set. They are employed in the representation of many biochemical systems at various levels from molecular representation , to protone-to-many in contrast to the original formulation that is referred to as one-to-one. Techniques for solving the one-to-one problem are mainly based on heuristics to speed-up the searching of a mapping function. Instead, the main efforts for solving the one-to-many problem are focused on developing a good filtering strategy for discarding target graphs belonging to the collection that do not contain the query graph. In particular, the most effective methodology for filtering strategy is the creation of an index in which features of the target graphs are stored. Then, when searching for a specific query graph, the target graphs are filtered by comparing the features of the query to those of the target graphs via the index. Thus, indexes are aimed at providing a very compact representation of the set of features and their correspondence to the target graphs. Performance in terms of construction time, size, querying time and filtering power are key concepts for their development. Such a performance is strictly related to the type of feature that is taken into account.The subgraph searching problem consists in finding a query graph within a target graph. It is a well-studied computational problem which is known to be NP-complete . A generpure subgraph isomorphism and assisted solvers. The first category is composed by algorithms that are focused on improving the performances of the combinatorial search by exploiting heuristic methods for pruning the search space, such as VF2 43], authhttps://github.com/qBioTurin/grapes-dd and https://github.com/InfOmics/grapes-dd.A performance study  finds thGraph indexing strategies based on labelled paths consist in extracting all the paths in the graphs up to a given length (number of nodes which they are composed) and compactly storing them into a data structure , 49, 50.In what follows, we describe one of these techniques, called GRAPES . GRAPES GRAPES  providesp of length l is a vector i labels, Formally, a path in a graph is an ordered sequence of vertices such that each vertex is connected with the next vertex by an edge. Thus, given a graph During querying phase, labelled paths are extracted from the query. Similarly to the extraction of paths from target graphs, for each path the number of times it occurs in the query graph is retrieved. Initially, all the target graphs are candidates to contain the query graph. Query paths are searched in the index in order to recognize the target graphs that contain the same paths of the query. For each path, the number of occurrences within the target graph must be equal or exceed the number of its occurrences in the query graph. By using the starting nodes of the paths stored in the index, the initial structures of target graphs are skimmed in order to extract only the vertices that are the starting point of paths in the query graph. Thus, the filtering procedure produces two different results, a list of graphs that may contain the query (since each selected graph contains the same labelled path of the query with the same amount), and for each selected graph the list of vertices that are candidate to match with the query vertices. The verification phase is performed with the VF2 algorithm  which soDecision diagrams (DDs) are a broad class of data structures widely used to encode and manipulate functions efficiently . Initialterminal or non-terminal. A terminal node has no outgoing edges and is labeled with a constant m is labeled with a variable i-th child of node m as child, with In this paper we will focus on a specific type of decision diagram, which is called Multi-Terminal Multi-way Decision Diagram (MTMDD). Formally, an MTMDD is a rooted, directed, acyclic graph representing a function n values . An MTMDSS.A simple example of MTMDD is reported in Fig. ordered MTMDD (OMTMDD) when a total order is defined on its variables  if it contains neither redundant nor duplicated nodes. A redundant node is a non-terminal node p and q, colored in cyan, are an example of duplicate nodes, while r and s are an example of redundant ones. The corresponding ROMTDDD (i.e. without redundant and duplicated nodes) is instead reported in Fig. In Fig. A common variation of the above reduction rule is to allow redundant nodes but no duplicate nodes. An OMTMDD is called quasi-reduced (QROMTMDD) if it contains no duplicate nodes and if all paths from the root node to a terminal node visit exactly one node for each variable. In Fig. unique-table [Many DD packages implement the reductions stated above storing all the DDs in a single, multi-rooted graph structure, known as ue-table , where nMTMDDs can be manipulated applying the arithmetic operators . Let ed-table  to cacheentclass1pt{minimaforest. Within a forest, Meddly automatically removes duplicate nodes by means of a unique table, imposes reduction rules and handles memory management of the nodes.In the literature different software libraries implementing decision diagrams were proposed, such as CUDD , LibDDD basic interface which provides the basic operators to easily create and manipulate DDs, and an expert interface which allows user to extend the existent operators and/or to integrate new ones. In this work, we implemented our tool by taking advantage of the basic interface of Meddly; in particular the following operators were exploited:F and some variable assignments Y, W, Z, a, b, c) creates a new DD within F representing the function createEdge creates a new DD in the given forest by explicitly stating the return values for a set of variable assignments. Unspecified assignments are assumed to return a default value, which depends on the forest type . For example, given the forest dd.evaluate determines the value of the function represented by the DD for a given assignment of its variables. Then, the call dd.evaluate makes it hard to exploit other types of symmetries present in the indexed graphs, as for instance the sharing of the same starting vertices and/or same relative occurrence number, as well as the sharing of common substrings which are not prefixes.To deal with these aspects, in this work we propose to encode the indexed graphs into a DD, specifically an MTMDD: a trie generalization in which the requirement to have a tree structure is relaxed allowing multiple arcs to point to the same node. The main advantage of this is a potentially more compact representation due to the MTMDD ability to better exploit the regular structure of the data, such as common substrings present in the indexed graph paths. This allows the proposed methodology not only to reduce the memory utilization required to build and store the index, but also to reduce the time required for the pruning phase.index building phase in which an MTMDD indexing the collection of target graphs is created, and (2) the filtering phase in which, given a query graph, the set of target graphs is potentially restricted to those sub-graphs probably containing the query. The GRAPES-DD verification phase remains as in the original version of the software , which is shown in Fig. The algorithm initially builds the pruned MTMDD the result of such multiplication, which is depicted in Fig. The multiplication operator (using the apply function) is applied between the index and the query MTMDDs, in order to extract from the index the information about the vertices really involved in the current query. We called v of a target graph v and that (2) the occurrence number of each path in the target graph is not less than the occurrence number of the same path in the query graph. Figure The set of candidate vertices obtained is then filtered to keep only those graphs whose vertices satisfy the constraints imposed by the query. For each vertex Finally, for each vertex of the query, the algorithm reports the list of the matchable vertices of the indexed graphs passing the pruning phase. The overall effect is that the algorithm extracts from the graph collection all the maximally connected components composed only by the vertices involved in the query graph. Over these components, the GRAPES subgraph isomorphism algorithm can be executed to find all the occurrences of the query graph .For this study, we considered six different types of graphs. Four of them are real graphs widely used as a benchmarks in the fields of bioinformatics and computational chemistry, the others are synthetically generated by means of the Barabasi–Albert’s and the Forest-Fire models. the choice of such two synthetic models has been taken according to their properties of the topologies to be similar the graphs used in biological databases. Differently from collections of real graphs, synthetic topologies allow us to investigate the performance of compared methods in relation to the parameters of such models, and thus to the properties of the produced topologies.The collection of biochemical graphs was initially used for evaluating the performance of one-to-one subgraph isomorphism algorithms , and, noAIDS is the standard database for Antiviral Screen [l Screen , and it PDBS is a benchmark composed of 600 target graphs representing the topological structure of proteins [proteins , 64. VerPCM is composed of three-dimensional structures of protein, called protein contact maps [act maps . VerticePPI is a dataset of 20 protein-protein interaction target graphs of 5 different species: Caenorhabditis elegants, Drosophila melanogaster, Mus musculus, Saccaromyces cerevisae and Homo sapiens [ sapiens . VerticeFor all of the biochemical datasets, queries were extracted from the target graphs by fixing the desired number of edges, from 4, 8, 18–32, and such that the topological structure of the extracted graph reflects the properties of the graph of origin.i is k is the vertex degree and The Barabasi-Albert’s model is able to reproduce a graph with an observed stationary scale-free distribution, which reflects many of the structures that can be encountered in nature . Startinp as 0.1, 0.3, 0.5, 0.7 and 0.9.The second type of synthetic graphs were generated according to the Forest-Fire model , that isFor both synthetic benchmarks, query graphs were extracted from the generated target graphs. The extraction was performed by fixing the number of desired vertices, ranging from 4, 8, 24, 32–64, and by extracting all edges among the selected vertices.For more details regarding the two sets of synthetic benchmarks, the reader can refer to .memory peak, as well as the space needed to store the built index in the hard disk, reported as index size. In addition, we compared the running time required by the two approaches for building the index. The analysis was mainly focused on the index construction phase because it is the main difference between the two approaches. They share the same methodology for what concerns the matching phase. In addition, the filtering time can be considered negligible with respect to the total querying time.We evaluated the performance of GRAPES-DD, with respect to its predecessor GRAPES, by taking into account both space and time requirements. In particular, we focused on the amount of primary memory that the two approaches require during the execution, reported as https://github.com/qBioTurin/grapes-dd or at https://github.com/InfOmics/grapes-dd. Both the tools were implemented in C++ and compiled with gcc 6.3.0. Then, the experiments have been carried out on a server equipped with four processors AMD Opteron 6167 2.20 GHz and 502 GB of RAM. Since GRAPES is a natively parallel software while GRAPES-DD is sequential, the experiments were executed using GRAPES with a single-thread.Both GRAPES-DD and GRAPES have been containerized in a Docker  image inPandas framework available for Pythonp.Figures Results show that, independently from the label percentage and model parameters, the performance of GRAPES-DD improves as the number of vertices of the indexed graph increases. In fact, for graphs having less than 5k vertices, the memory peak required by GRAPES-DD is higher than the peak of GRAPES, resulting in a ratio between the two values less than 1. The out-performance of GRAPES-DD can reach two/three orders of magnitude with respect to GRAPES, for graphs with 20k vertices, which means that the memory requirement of GRAPES-DD is one hundredth that of GRAPES.Similar trends are observed for the size of the index when it is stored into the hard disk. In this case, the ratio can achieve five orders of magnitude as it is shown for the Forest-Fire graphs with 20k vertices. In general, best ratios are obtained for the Forest-Fire graphs with a high number of vertices, however, this behaviour is counterbalanced by the fact that on average Forest-Fire graphs with less than 5k vertices are also those with the lowest ratios.For what concerns the memory peak, we can observe that the label percentage is a more crucial factor for the Barabasi–Albert model rather than for the Forest-fire model. More in general, a low label percentage is to the advantage to the trie structure of GRAPES because the extracted paths share and relatively high number of labels. Opposite trends are observed for what concerns the storing of the index.p parameters produce more dense graphs.As for the label percentage, model parameters produce less variation compared to the number of vertices. The Barabasi-Albert model produces scale-free networks where the distribution of the degrees of the vertices follows a power law. A value greater than 1 increases the skewness of the resultant distribution, while a value less than 1 flattens the distribution. Thus higher values trend to produce a more sparse graph. Results in Fig. GRAPES-DD reaches an average indexing compression ratio of 11.16 with respect to GRAPES when Barabasi-Albert networks are indexed. Instead, an average ratio of 9.46 is reached over the Forest-Fire collection. The better ratio obtained by GRAPES-DD highlights that the application of MTMDDs is advantageous for any of the two types of random graphs, however, it is more suitable for Barabasi-Albert networks that are considered more similar to biological networks.p model parameters. As for the previous analysis, the ratio is computed by dividing the values measured for the trie structure of GRAPES with those registered for the MTMDD of GRAPES-DD. As it has been shown for the single-graph analysis, the percentage of distinct labels with respect to the total number of vertices in the graphs) is a discriminant factor for the compression gain obtained by the MTMDD structure. Also the trends relative to the parameters of the models are confirmed. In general, the MTMDD structure is on average more convenient on the Forest-Fire graphs for what concerns the memory peak. Barabasi-Albert graphs with Subsequently, we evaluated the performance of exploiting the MTMDD structure for indexing 14 collections of synthetic graphs  which only includes paths of length 4 was taken into account. We were not able to run CT-Index on the PCM and PPI datasets due to excessive memory usage of the tool.Collections of biochemical graphs were also used for evaluating the performance of GRAPES-DD during the querying phase in comparison with exiting approaches VF2  and CT-IFigure On AIDS collection see Fig. a, GRAPESThe VF2 algorithm is outperformed by GRAPES-DD in the PDBS, PCM and PPI collections see Fig. b–d. MoreRegarding the PDBS collection see Fig. b, GRAPESConsidering the PCM collection see Fig. c, GRAPESOn the PPI collection see Fig. d, GRAPESV| and number of edges |E|, results in a better performance of GRAPES-DD with respect to GRAPES. However, comparing the ratios obtained for M. musculus and H. sapiens we can deduce that as expected there is not a fixed correlation between the graph size and the performance. Therefore, the intrinsic nature of the graph is also responsible for these results. PPI networks are also the targets for which running times of GRAPES-DD are comparable to those of GRAPES, and some times they are even better. The GRAPES-DD building approach includes the construction of partial tries but without merging them. The cost for traversing a single whole trie may limit GRAPES.Lastly, Table In this study, we deal with the problem of reducing the indexing size of biochemical and biological graph searching systems to make them effective with the increasing size of the structures. We show that the indexing of labelled graphs can take the advantages of newly adapted data structures based on decision diagrams. These techniques allow already existing methodologies to increase their compression power, in terms of memory consumption, without significantly increasing the searching time requirement.We examined synthetic graphs because they offer a more systematic way of investigating performance of indexing using decision diagrams. Since the type of the generated graphs reflects the structures that are found in nature, their analysis can be exploited for inferring performance behaviour of real biochemical and biological structures. The results showed that relevant indexing compression ratio can be obtained in relation with the size and the topological structure of the graphs and the distribution of labels within them. Moreover, the larger are the indexed graphs, the higher is the advantage of using Decision Diagram data structure.A well-established benchmark was also used for evaluating the performance on real graphs. The size of the considered graphs are relatively small, compared with the synthetically generated ones, however trends of gain ratio are confirmed. This must be considered in the perspective of future applications of the proposed indexing technique, because the continuous development of new technologies for extraction biological information leads to the construction of biological relational systems that constantly increase in size. In addition to the gain in compression ratio, GRAPES-DD outperforms GRAPES in terms of build times while maintaining comparable query times. Furthermore, our analyse show that graph search approaches based on indexing, in graphs of some complexity, can amortize the overhead of building indexing data structures at query time.Nowadays, graphs are fundamental structures for representing and for investigating the current biological and biomedical knowledge. In this work we investigated the possibility to improve the performance of the cutting-edge algorithms for searching substructures in graphs based on indexing, by addressing one of their disadvantages which is the size of the index. To this aim, we developed GRAPES-DD, a new version of GRAPES tool, whose strength is the use of decision diagrams to substantially reduce the size of the index. Experimental results performed on a set of synthetic and real benchmarks reported clearly that the use of this data structure allows us to substantially reduce the memory footprint of the index  with respect to the original version of GRAPES without impacting the running time of the algorithm.Further enhancement of GRAPES-DD will be to re-implement the building phase to allow thread-based parallelization as in the original GRAPES implementation. Moreover, since the efficacy of decision diagram techniques is strictly dependent on the variable order, we will investigate how different algorithms for variable orderings behave and we will evaluate the possibility of developing meta-heuristics to identify a-priori the best variable ordering depending on the features of each target graph.


I = 0.477) within the population, illustrating a good source for wheat improvement. The average PIC of 0.201 demonstrates moderate genetic diversity of the present evaluated advanced breeding panel. Analysis of molecular variance (AMOVA) detected 1% and 99% variance between and within subgroups. It is indicative of excessive gene traffic (less genetic differentiation) among the populations. These conclusions deliver important information with the potential to contribute new beneficial alleles using genome-wide association studies (GWAS) and marker-assisted selection to enhance genetic gain in South Asian wheat breeding programs.Genetic diversity and population structure information are crucial for enhancing traits of interest and the development of superlative varieties for commercialization. The present study elucidated the population structure and genetic diversity of 141 advanced wheat breeding lines using single nucleotide polymorphism markers. A total of 14,563 high-quality identified genotyping-by-sequencing (GBS) markers were distributed covering 13.9 GB wheat genome, with a minimum of 1,026 SNPs on the homoeologous group four and a maximum of 2,838 SNPs on group seven. The average minor allele frequency was found 0.233, although the average polymorphism information content (PIC) and heterozygosity were 0.201 and 0.015, respectively. Principal component analyses (PCA) and population structure identified two major groups (sub-populations) based on SNPs information. The results indicated a substantial gene flow/exchange with many migrants (Nm = 86.428) and a considerable genetic diversity (number of different alleles, Na = 1.977; the number of effective alleles, Ne = 1.519; and Shannon’s information index,  Triticum aestivum L.) is an allohexaploid species originating from successive rounds of hybridization in the Fertile Crescent during the Neolithic time, ∼8,000 to 10,000 years ago  based markers, which gave a better view of underlying diversity markers due to their abundance and environmentally neutral nature . Among vMicroarrays have been used as a pre-eminent answer to develop SNPs in polyploid wheat genomes . Once anIn the current study, GBS was used for genotyping 141 elite advanced breeding lines of spring wheat from CIMMYT (Mexico). The objectives were to illustrate the population structure and genetic diversity within and among subgroups. The present study not only defines the population structure and genetic diversity in these elite advanced wheat breeding lines also places a groundwork for genome-wide association study in this panel.One hundred forty-one advanced breeding lines (ABLs) developed at CIMMYT, Mexico using adapted cultivars from various worldwide breeding programs were used in studying genetic diversity. These lines have been selected from a more extensive set of advanced breeding lines sent to South Asia as part of the CIMMYT wheat breeding program to develop high-yielding varieties suitable for the region. The lines were introduced in 2016 as the 4th cohort of the South Asia Bread Wheat Genomic Prediction Yield Trial (SABWGPYT). The lines were developed by making crosses with diverse and high yielding parental lines as part of the CIMMYT Global Wheat Program (GWP). A set of approx. thirty-eight thousand lines were planted in small plots in Obregon during the 2015-16 crop season. The selection was made based on yield and agronomic traits. Then lines from different genetic backgrounds were carefully selected as candidate varieties suitable for further testing in South Asia. The pedigree details data of those 141 advanced breeding lines are given in Genomic DNA was extracted using the modified CTAB method from the fresh leaves of wheat seedlings . GenotypThe genotypic summary of the 14,563 SNPs was obtained using the “geno summary” function of TASSEL v5.2.6 . ChromosThe population structure of the advanced bread wheat breeding lines was inferred using the Bayesian method implemented in STRUCTURE 2.3.4 . Ten indThe selected K value from structure output was used to subdivide the advanced wheat lines into sub-populations and was utilized for AMOVA. AMOVA and genetic diversity indices were performed for individual sub-population using GeneAlEx v6.41 . The perI), effective alleles (Ne), different alleles (Na), number of loci with private alleles, unbiased diversity (uh), and the haploid genetic diversity index (h) were also performed.The calculation of Shannon’s Information Index  using the formula. Here p is the frequency of the allele. i, i.e., frequency of ith allele, private alleles are the alleles unique to the subpopulation. The haploid genetic diversity index (h) provides the probability that the two individuals would be different and was calculated using the formula. Where ln is the natural logarithm of pThe unbiased diversity (uh) was calculated using the allele frequency and sample size (n) with a formula. https://doi.org/10.6084/m9.figshare.14273339.v2.). The Chinese Spring Wheat Assembly (RefSeq v1.0) reference genome assembly used in the present study is a widely used reference genome in hexaploid wheat. (https://urgi.versailles.inra.fr/download/iwgsc/IWGSC_RefSeq_Assemblies/v1.0/ and https://urgi.versailles.inra.fr/download/iwgsc/IWGSC_RefSeq_Annotations/v1.0/). The B-genome had a maximum of 7378 SNPs (50.66%), subsequently the A-genome with 5921 SNPs (40.66%) and the D-genome with a minimum number of SNPs, i.e., 1264 (8.68%)  used for present analysis have been provided through the link ( (8.68%) , Fig. 1A (8.68%) . The SNPThe heterozygosity among all SNPs ranged from 0.00 to 0.149. Twenty-one SNPs showed high heterozygosity (>0.10), and 3,095 SNPs showed zero heterozygosity . The hetK = 2, indicating the presence of two major subgroups  in the subgroup 1 (G1) and subgroup 2 (G2) were 1.954 and 2.0, respectively, indicating comparatively better diversity in G2 than G1 (The mean Shannon’s information index (I), the diversity index (h), and the unbiased diversity index (uh) were high , which indicated its high diversity , Fig. 4. than G1 .I, h and uh for G1 & G2 were 0.468 and 0.487, 0.307 and 0.318, 0.320 and 0.321, respectively, indicating relatively higher diversity within G2 than G1.Similarly, the numbers of effective alleles (Ne) was relatively higher in G2 (1.525) than in G1 (1.512). However, only G2 showed a significant percentage of private alleles, i.e., 4.6%. G1 contained 22 members with private allele ranging from 7 in GID7399640 to 149 in GID7400491, while G2 contained 90 members with private alleles ranging from 3 in GID7396143 to 188 in GID7400293 . The divA panel of 141 genotypes from an extensive collection of elite advanced bread wheat breeding lines from CIMMYT, Mexico, was used to study the genetic diversity . The higAe. tauschii (D-genome donor) (T. aestivum and T. turgidum (AABB); however, not amongst Ae. tauschii (DD) and hexaploid  . Signifiexaploid . It leadexaploid . The rolexaploid . The smaexaploid . While texaploid  and 1B (exaploid .The low levels of observed heterozygosity (0.00−0.149), with approximately 97% of SNPs having heterozygosity <0.05 and only 21 SNPs having heterozygosity above 0.10, showed the panel had high genetic stability . Since tPIC values are signals of informative markers in the crops and reflect through the spreading of informative markers in the genome, which can be used for studying genetic diversity . The modK = 2, which is vital for the elucidation of genetic diversity. In the present study, the STRUCTURE analysis was identified by ΔK with the highest peak at K = 2, G1 and G2 contained 22 and 90 members with private alleles, respectively, illustrating a clear difference in the lines containing private alleles  regardless of being low (1%). The present study results will aid breeders to understand the genetic diversity and perform marker-assisted selection on this panel.The AMOVA results showed high genetic diversity within-subgroups; however, the diversity between subgroups was very low (1%). The result may be due to the common parental backgrounds and selection based on designated agronomic traits resulted in high gene flow levels. This low level of variation among the stratified groups occurs due to increased gene exchange described by In the present study, we applied GBS-based SNP to learn GBS-SNP markers’ usefulness for diversity analysis in 141 elite wheat breeding lines. Despite very designated, our advanced breeding lines panel was found to be genetically diverse, which could be instrumental for future South Asian breeding programs to develop new elite wheat varieties of alluring traits, i.e., high yield, biotic and abiotic resistance. Besides, the present study dappled two subgroups that were enlightened by their parentage and selection history. The low heterozygosity detected among elite advanced wheat breeding lines within subgroups and the moderate divergence among subgroups suggested that the elite advanced wheat breeding lines could be used further for GWAS studies.10.7717/peerj.11593/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.11593/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.11593/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.11593/supp-4Supplemental Information 4Pop1 is subgroup1, pop2 is subgroup2, column D contains the names of private alleles in respective lines.Click here for additional data file.10.7717/peerj.11593/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.11593/supp-6Supplemental Information 6Click here for additional data file.


Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.Though  They have also been an important source of human food. Due to climate change and other anthropogenic factors, global kelp populations have faced a drastic decline . The impSaccharina japonica breeding strategies that have been used in the past involved selfing, which leads to loss of genetic diversity (Saccharina latissima and apply genomic selection (GS), with the aim of improving breeding efficiency and predicting combining abilities of parental gametophytes (GPs) using the sporophyte (SP) performance. As the first breeding program for sugar kelp in the Northeast United States, we initiated a selective breeding system by evaluating uniclonal plots.Kelp cultivation has been established for over 60 years in Asian countries. More recently, there is growing interest in macroalgal cultivation in Europe, South America, and North America  among founders. The objective of this study was to determine the impacts of overcoming these 4 obstacles on genetic gain over 10 years of breeding effort. These obstacles are further explained in In 2018, we collected SPs from the wild throughout the Gulf of Maine (GOM) to southern New England (SNE) in the United States , and our repeated breeding cycles start from year 1, for both the 2-year cycle and 1-year cycle breeding schemes . A full Obstacle 1  in the breeding program . First, Obstacle 1 and complete a breeding cycle in 1 year would entail some combination of the following:The technical advancement to overcome Methods to enhance the growth rate of the GPs so that GPs sampled in the spring have sufficient biomass to make crosses in the fall.Methods to make crosses that require less GP biomass but that nevertheless produce plots with adequate numbers of SPs.In our empirical breeding program, we worked with CycleTime = 2 years. We simulated scenarios where we have a 2-year cycle time and where we could reduce the time from 2 years to 1 year.Obstacle 2 , and seasonal capacity in the hatchery for maintaining GP crosses and the resultant individuals of uniclonal juvenile SPs for 4–6 weeks before out-planting. We simulated scenarios assuming we could design space and labor-saving methods for the laboratory/hatchery phase and manage higher throughput phenotyping to evaluate NumCross = 1,000 plots instead of NumCross = 400 plots each year.Obstacle 3  in more than 25% of the collection sites. Markers with minor allele frequency <0.05 and individuals with more than 50% missing data were also removed. A final set of 4,906 markers was retained and imputed using the rrBLUP package A.mat function (Ne), according to c is the recombination rate among all sites assumed to be 0.5, given that most pairs of sites are on different chromosomes. This gives an estimated Ne to be underestimated , jth check groups, where each check variety is a unique group whereas all testing crosses were assigned as 1 combined group, kth block and lth line effects nested within year, respectively, nth individual  nested within jth check group. All effects were treated as fixed except for SP genetic effects. A relationship matrix was included using the rrBLUP mixed.solve function in R to obtain the additive variance for SPs. Any interaction between genotype and environmental variation from year to year  would be part of the error variance. Narrow sense heritability was estimated using:We used empirical data to estimate trait heritability for the simulation. A mixed model, including genetic effects of SPs as random effects, and growth line, blocks, year effects, and reference checks as the fixed effects, was conducted to estimate the narrow sense heritability using GOM farm SP plots data from 2019 and 2020 field seasons. The following model was used:S.japonica  is NumCross*(i − 1), and the end training size by Year 10 is NumCross*9. All GPs coming out of the flow cytometry process would be genotyped. The GS model used ridge regression best linear unbiased prediction , total genetic variance (estimated via the function varG in AlphaSimR), and GS prediction accuracy via ANOVA.Each scheme was simulated 20 times, and the average genetic gain as well as genetic variance at each GP stage was calculated over 10 years. Because we were mainly interested in evaluating the trend of genetic gain from different breeding schemes, the reference point for genetic gain could either be for GPs or SPs and we used the GP stage. The additive genetic variation, selection accuracy, selection intensity, and breeding cycle time can affect genetic gain:cle time . The traOur number of GPs per SP (nGP) was either 24 or 96. At any given generation of SPs, 10% of lines were selected either at random or by phenotypically selecting the top performers. The selection intensity under random selection is 0, and selecting the top 10% assuming a normal distribution causes the selection intensity to be 1.75. When selecting among GPs, with a scenario of nGP = 24, the proportion of FG and MG being selected is 41.6% [NumCross/(10%*NumCross*24)]. Again assuming normality, the selection intensity is 0.94. When nGP = 96, the proportion being selected is 10.4% [NumCross/(10%*NumCross*96)], generating a selection intensity of 1.74 . To validate the normality assumption, we evaluated empirical selection intensity by calculating the standardized mean difference between the selected population and the reference population. These empirical intensities were similar to the theoretical values (data not shown).h2 levels.The analysis of variance (ANOVA) was conducted to evaluate effects of SelectSP, NumCross, CycleTime, and nGP as well as their interaction effects on changes of genetic mean, genetic variance, and GS accuracy, respectively, over 10 years of breeding time. The ANOVA was reported at each of the Ne and The ability to exert selection on the farm-evaluated SPs (SelectSP), the number of years per breeding cycle (CycleTime), and the number of GPs per SP surviving the flow cell cytometry system (nGP) were the 3 significant contributors to the changes of genetic mean over time . We did Obstacle 4  and decreasing the breeding cycle time  led to gain increases of 118% and 73%, respectively, averaged across all other factors. Though the effect of increasing the number of plots phenotyped  was not statistically significant , and then by reducing the time for obtaining sufficient GP biomass such that a 1-year cycle is enabled compared with our current 2-year cycle . Increasing the number of viable GPs we obtain per parental SP  also delivered significant gain, while somewhat surprisingly, phenotyping more SP plots  did not. These conclusions were not affected by the founder population effective population size or the trait heritability. Thus, the clear direction to prioritize breeding enhancement is to improve our ability to induce SP spore release and to modify GP culture to accelerate growth. In addition, we should experiment with the amount of GP biomass needed to make sufficient SP progeny. We may not need a full 1-m length of plot for evaluation.The simulation revealed a robust result that when other factors remain the same, the highest genetic gain can be achieved by exerting selection on SPs phenotypically rather than randomly . Ideally, we aim to select crosses in the top 10% for performance and then artificially induce sporulation in the laboratory. This has proved successful on a small scale if desirable SPs are identified within a day of harvest. Yet compared with the large number of SPs plots we currently have, overcoming Obstacle 3 requires greater investment in labor to identify and separate candidate SPs and investment in additional culture space to accommodate the induction process. Practically, we have also observed that not all SPs could generate GPs in the laboratory, which could be due to sterility related genes, laboratory environmental conditions, or both. Hence, understanding the mechanisms causing sterile kelp would be beneficial in addressing this obstacle too.It can be challenging to induce top performing SPs to release spores , which could also be the hardest task. In brief, it takes 4–8 weeks to induce immature SPs to full maturity and release meiospores under artificial conditions in the laboratory  that would allow better light penetration. To test if a 1-year cycle time is feasible in our current breeding program, we have also experimented with using a minimum amount of biomass to make crosses and generate at least a single SP blade. The function of this blade would not be for evaluation of SP performance, but for recombining the best GPs in the hope of getting improved recombinants. This approach would generate phenotypic data on the individual SP blade but not on biomass per meter of line, which is a plot-level trait. Hence this procedure would not be a full representation of the 1-year per cycle scheme we simulated here.Another possibility that is used in forage breeding (Obstacle 2 (nGP) by increasing the number of GPs per parental SP can potentially be done easily. A simple approach would be to increase the number of plates automatically sorted by flow cytometry per parental SP, which would increase the number of GPs in the GS step, allowing higher selection intensity. Nonetheless, this would result in increasing the number of cultures to maintain in the laboratory, which leads to more labor and cost. The use of flow cytometry sorting expedites the initial isolation process, but the parameters that determine the survival of spores are not well understood. The condition of sorus tissue prior to spore release and sorting likely influences spore viability. Percentage viability varied across samples presumably because of differences in sorus tissue condition and handling prior to sorting (Overcoming except increasing the number of phenotyped plots (NumCross) which both increased gain and decreased variance lost  could be accomplished without new research, but would be costly since it would require more GP grow-out space and labor. This change generated only a small increase in the rate of genetic gain. An important benefit to increasing the number of SPs being phenotyped, however, was that it maintained genetic diversity and slowed down the decrease of genetic variance  and 4. Tnce lost . We alsoWhile in this discussion we have treated heritability as fixed, that is not strictly true. Heritability might be increased if we could improve our planting technique to ensure that plots are more uniformly covered by SPs, so that we obtain successful and uniform growth of SPs in the field. Not surprisingly, higher heritability leads to greater final gain  and 3. TOverall, selection causes variance decreases both because of the Bulmer effect and because high fitness ancestors contribute disproportionately to descendants. In our simulations, genetic variance dropped substantially . This drIn summary, our short-term gain improvement would be achieved if we could improve our ability to induce spores to generate GPs from top selected SP plots on farm, reduce the amount of time it takes for GPs to be ready for crossing, and increase the number GPs generated per SP. These means we could exert a positive selection on SPs and reduce the breeding cycle time. Form a long-term perspective, however, it is beneficial to increase the number of plots being tested on farm, which helps maintain the genetic diversity. The creation of new variation such as through collecting wild samples should also be an ongoing effort in the program. Overall, the robustness of these simulation findings should give us confidence in the research directions they suggest. We believe that these priorities will help accelerate genetic gain in breeding programs and therefore increase the value of kelp farming in the United States and globally.https://github.com/MaoHuang2020/SimulationKelp/tree/master/For_G3. All data necessary for confirming the conclusions of the manuscript are present within the article, figures, and tables.G3 online.Supplemental material is available at jkac003_Supplemental_Tabes_1_and_2Click here for additional data file.jkac003_Supplemental_Figure_1Click here for additional data file.


With a growing human population, global climate change and diminishing natural resources, the need for sustainable agricultural systems is evident. Insect farming has been shown to be an envi-ronmentally friendly alternative to conventional livestock farming. The black soldier fly (BSF) is a species of interest because the larvae are effective bioremedial agents, feeding on organic waste and converting it to usable animal derived products that have a similar nutrient profile to conventional feedstuffs like soy and fishmeal. This creates an opportunity for renewable food production systems. Managing genetic diversity in commercial insect populations is important for the long-term viability and productivity of the colony to mitigate any negative effects of inbreeding. In this study we in-vestigated the effects population dynamics and breeding behaviour on the genetic health of a mass reared BSF colony. The results suggest significant loss in genetic diversity and rapid divergence of captive populations from ancestral wild ones. The study also gives the first evidence for multiple paternity in BSF. The results will contribute to establishing effective genetic management strategies for BSF farming, ensuring long term sustainability of a new industryHermetia illucens, is a promising candidate for the emerging insect farming industry with favourable characteristics for both bioremediation and production of animal delivered nutritive and industrial compounds. The genetic management of commercial colonies will become increasingly important for the sustainability of the industry. However, r-selected life history traits of insects pose challenges to conventional animal husbandry and breeding approaches. In this study, the long-term genetic effects of mass-rearing were evaluated as well as mating systems in the species to establish factors that might influence genetic diversity, and by implication fitness and productivity in commercial colonies. Population genetic parameters, based on microsatellite markers, were estimated and compared amongst two temporal wild sampling populations and four generations  of a mass-reared colony. Furthermore, genetic relationships amongst mate pairs were evaluated and parentage analysis was performed to determine the oc-currence of preferential mate choice and multiple paternity. The mass-reared colony showed a reduction in genetic diversity and evidence for inbreeding with significant successive generational genetic differentiation from the wild progenitor population. Population-level analysis also gave the first tentative evidence of positive assortative mating and genetic polyandry in BSF. The homoge-neity of the mass-reared colony seems to result from a dual action caused by small effective popu-lation size and increased homozygosity due to positive assortative mating. However, the high ge-netic diversity in the wild and a polyandrous mating system might suggest the possible restoration of diversity in mass-reared colonies through augmentation with the wild population.The black soldier fly (BSF),  Hermetia illucens   has increased considerably in recent years )gynandry . As all Hermetia [Black soldier flies exhibit lekking, a mating behaviour where males aggregate at sites known as leks, each defending their own small territory. Females then visit these lekking sites for the sole purpose of mating . Fly speHermetia . As lekkHermetia ,43,44,46Genetic diversity may also be influenced by the presence of selective mate choice. In populations exhibiting random mating, genotypic frequencies remain stable over time. However, when sexual selection occurs, equilibrium genotypic frequencies are disrupted, potentially affecting genetic diversity . NegativIt has been observed that BSF females only produce a single viable clutch of eggs in their lifetime , possiblGiven the interplay between genetic diversity and mating systems, this study aimed to particularly assess the maintenance and long-term trends of genetic diversity in an established, mass-reared colony of BSF, as well as to evaluate mating behaviour in a mass-reared colony via mate pair genetic comparisons and parentage analysis.2015) and 2018 (Wild2018), respectively. These cohorts represent the wild founding population of the mass-reared colony. The mass-reared colony itself was established in 2012, from an unknown number of founding wild flies. Since establishment, the colony was maintained under industry standard mass-rearing production protocols with no additional population augmentation with wild flies during latter generations. Each generation was reared discretely with no overlapping or admixture between generational cohorts. At the start of the experiment the colony was at the 28th generation. To assess long-term trends, sampling occurred approximately every 15–20 generations with one “intermediary” sampling event (F52). As such, 30 adult flies per generation were collected from generations F28, F48, F52 and F62 of a mass-reared colony under factory conditions. Each fly was stored individually in a tube containing 90% ethanol, at −20 °C. Parts of the head, thorax, and legs of individuals were removed using a sterile surgical scalpel for genomic DNA extraction. A modified version of the cetyltrimethyl ammonium bromide (CTAB) extraction method, with the addition of proteinase K to accommodate the extraction of DNA from insects, was used [TM ND 1000 spectrophotometer . Working dilutions with a final concentration of 20 ng/µL were prepared and stored at −20 °C.Assessment of genetic diversity: Two sampling cohorts of 30 adult flies each were randomly selected from a wild BSF population in Durban, South Africa  in 2015  for 30 s, and an extension step at 72 °C for 90 s. Reactions were concluded with a final extension at 72 °C for 30 s. Successful amplification was confirmed through visualisation via 1.5% agarose gel electrophoresis, 100 Volts for 45 min. Samples were then diluted with double-distilled water at a ratio of 3ddH20: 1DNA before capillary electrophoresis at the Stellenbosch Central Analytical Facility’s DNA Sequencing Unit. Alleles were scored from chromatogram data using GeneMapper v5.0.3  and a GeneScan™ 500 LIZ®  DNA ladder standard.Eight microsatellite loci previously developed by Rhode et al.  were ampNA), effective number of alleles (EA), observed and unbiased expected heterozygosity , Shannon’s information index (I), and per locus ISF were calculated using GenAlEx v6.503 [ISF) for each group was determined by calculating the average of per locus ISF estimates. The allelic richness (RA) and private allelic richness (RPA) of each cohort were determined by implementing the rarefaction technique to correct for sampling bias in HP-Rare v1.1 [p < 0.01) in XLSTAT [r) was also calculated in GenAlEx following the Queller and Goodnight method [Assessment of genetic diversity: Input files for relevant software were first created using Microsatellite Toolkit v3.1 . Micro-cx v6.503 . A mean are v1.1 . Next, tn XLSTAT . Mean wit method , with stThe effective population size of each generation was calculated using the linkage disequilibrium (LD) method in NeEstimator v2.01, with a 95% confidence interval . A randoSTF estimates between the six cohorts . A hierarchical analysis of molecular variance  was also performed in Arlequin. Samples were separated into two groups: a wild group containing Wild2015 and Wild2018 and a mass-reared group containing generations F28, F48, F52 and F62 of the mass-reared population. To visualise the genetic differentiation between populations, a discriminant analysis of principal components (DAPC) was performed, using the R package adegenet [To determine the level of genetic differentiation between the wild sampling populations and the mass-reared colony, as well as generational genetic differentiation across the temporal scale for the mass-reared colony, Arlequin v3.5.2.2  was usedadegenet . Cross-vEH and OH, respectively) and per locus ISF were calculated for three sampling populations: the candidate parents (F0), the offspring (F1) and the colony that the parents were sourced from (S). This was done in GenAlEx v6.503 [p < 0.01), to test for significant changes between the source population and offspring. The Queller and Goodnight method was then used to calculate the mean relatedness (r) in the source population and the candidate parent generation, as well as the individual pairwise relatedness estimates within each of the five parent pairs [Assessment of mating behaviour: Input file preparation and basic genotypic quality control were done as described above. Due to the small number of markers used, individuals with missing data at two or more loci were removed from the dataset. Genepop on the web v4.7 ,63 was ux v6.503 . These ent pairs . This waEach of the five families was tested for multiple paternity separately using two different methods: genotypic exclusion and full-pedigree likelihood. Vitassign v8.2.1  was usedp < 0.05). The number of alleles (NA), allelic richness (RA), and private allelic richness (RPA) of generation 62 also differed significantly from the two wild samples. The mass-reared colony suffered its greatest losses of genetic diversity within the first 52 generations, showing signs of a slight recovery in F62 (ISF) showed a similar trend, with the exception of an abnormally low inbreeding coefficient in F48. All groups showed deviation from HWE. For a full list of results, see Assessment of genetic diversity and differentiation: Based on the eight microsatellite markers, significant differences in all diversity estimates were found between the two wild cohorts and generations 48 and 52 of the mass-reared population , as well as between individuals within each of the populations  and clustering separately on the DAPC plot from the Wild2015 cohort , a subgroup containing five parent pairs (F0) and their offspring (F1). A lack of significant differences in diversity estimates between the three groups , provideDrosophila flies have previously been found to be less competitive when closely- related and reared together. Less harm was caused to females during mating, increasing both mating success and their reproductive life span [Evidence for multiple paternity was found in two out of five families, with the genotypic exclusion method estimating two contributing fathers per family and the full-pedigree likelihood method estimating three. These findings provide evidence for the presence of polyandry in the species. Polyandry has previously been observed in various lekking insects, including fruit flies and moths ,96. The ife span . Thus, iThe occurrence of polyandry and resulting multiple paternity provides evidence that adult flies can mate multiple times with genetic consequences for the offspring, despite not being able to replenish energy between mating events. Thus, disproving the currently held hypothesis that although multiple mating has been observed in BSF as a behaviour, the energetic constraints on gametogenesis effectively render the species genetically monogamous. Polygyny is therefore also possible for this species but needs to be assessed in the future. Some lekking species display polygyny through the emergence of males with a fixed number of sperm, which is then divided amongst partners. Ejaculate size decreases with each successive mating event until sperm and energy reserves are depleted ,95.The occurrence of polyandry in the mass-reared colony has positive implications for its genetic management. Polyandrous mating allows for greater genetic diversity in offspring than monogamous mating, thereby aiding both the maintenance of genetic diversity and the reintroduction of genetic diversity into homogenous populations ,98.r-selected life history characteristics predispose the species to a rapid loss of genetic diversity that might hinder population fitness, productivity and future enhancement potential, as has been demonstrated in this study with a significant loss in genetic diversity in a mass-reared BSF population over the long-term. Furthermore, other than the classical microevolutionary processes associated with small and isolated populations, genetic diversity is also influenced by the dynamics of mate choice and breeding behaviour. The first evidence of positive assortative mating and multiple paternity for the BSF is presented here. The genetic management of BSF colonies will thus entail a careful interplay between managing colony demographic trajectories, mating systems, and selective breeding.The black soldier fly has shown particular promise as both a bioremedial agent and a source of usable animal protein and other bio-products, which has led to the establishment of large industrial production facilities. As domestication progress, particular emphasis is being placed on genetic management and selective breeding for the enhancement of production characteristics in commercial populations. However, unlike conventional livestock, insects, including the BSF, have very different life history characteristics that might prove challenging for conventional animal breeding methods. In particular,


The Guzerá breed is one of the most relevant animal genetic resources for tropical and extreme environments, that is, low and high temperatures, and dry or humid environments. These animals were selected for beef, milk, or dual-purpose (beef and milk), and were extensively used to produce crossbred animals. Consequently, subjecting the breed to intense genetic bottlenecks in Brazil. The local scientific community and breeders have pursued a sustainable management and conservation program over the last 50 years. To evaluate the efficacy of these efforts, we characterized genetic diversity and structure in a Guzerá meta-population. DNA samples of 744 animals from one dairy, nine dual-purpose, and five beef herds were genotyped for 21 microsatellite loci. The genetic diversity estimates suggested a low fixation index, revealing a large genetic diversity in Guzerá herds. The dual-purpose herds/selection lines are the most uniform subpopulation, while the beef one preserved larger amounts of genetic diversity, representing a genetic diversity reservoir for the breed. In addition, the dairy herd showed to be genetically distant from other herds. Taken together, these results suggest that this Guzerá meta-population has higher genetic diversity, a lower degree of population subdivision, and a lower inbreeding level.is, Fit, and Fst estimates were obtained considering either farms or lineages as subpopulations. Mean Ho (0.73) and PIC (0.75) suggest that genetic diversity was efficiently conserved. Fit, Fis and Fst values (95% CI) pointed to a low fixation index, and large genetic diversity: Fit , Fis , and Fst . The dual-purpose herds/selection lines are the most uniform subpopulation, while the beef one preserved larger amounts of genetic diversity among herds. In addition, the dairy herd showed to be genetically distant from other herds. Taken together, these results suggest that this Guzerá meta-population has high genetic diversity, a low degree of population subdivision, and a low inbreeding level.The Brazilian Guzerá population originated from a few founders introduced from India. These animals adapted well to the harsh environments in Brazil, were selected for beef, milk, or dual-purpose (beef and milk), and were extensively used to produce crossbred animals. Here, the impact of these historical events with regard to the population structure and genetic diversity in a Guzerá meta-population was evaluated. DNA samples of 744 animals  were genotyped for 21 microsatellite loci. Ho, He, PIC, F Maximum conservation of genetic diversity is the best guarantee of long-time survival of a breed or a population under artificial selection. Significant loss of genetic variation due to Bulmer effect, inbreeding, and, especially in small populations, genetic drift in the populations under directional selection could imply in low response to genetic selection, decreasing genetic progress rate as well as adaptive performance ,2,3. On Bos indicus) cattle are originally from India, particularly from the dry lands of the Kankrej region. A relatively small population was brought from India at the end of the 19th century and expanded to Brazil, where it became adaptable to various, especially harsh, environmental conditions [Guzerá  [ [9] obtaIn 1993, FAO included the Guzerá in the World List for Domestic Animals Diversity. This list includes breeds of economic, scientific, cultural, and agricultural interest to be conserved through management. Motivated by the importance of this breed as a genetic resource, two focused, interconnected breeding programs were initiated: one for milk traits  other for beef traits .Nevertheless, because of the selection process based on genetic evaluation, further reduction in the number of sires used by breeders, due to the selection process based on genetic evaluation ranks, has become a concern. In the medium—and long-term, this could result in the reduced genetic variability of the population and increased levels of inbreeding ,9. From Genetic diversity and population structure have been studied in bovine populations using microsatellite markers ,16,17,18The main objective of this study was to ascertain genetic diversity and population structure in a meta-population of the Guzerá breed. A meta-population was defined in the current study as the fraction of the population to which we had access, that is, the subpopulation made up of herds that take part in the breeding programs and among which some degree of gene flow occurs. From the results of this study, we expect to effectively contribute to the sustainability of this breed by minimizing the loss of diversity and allowing long-term genetic progress.This study used blood samples from 744 animals, representing around 10% of the animals in each of the 15 major Guzerá herds located in Minas Gerais state, in southeastern Brazil. The majority of herds are concentrated in the Minas Gerais State, which provides proven genetic material from Guzerá nationwide. Currently, some herds exploit Guzerá animals for dual-purpose (dairy and beef), using proven sires from both breeding programs. Other herds use Guzerá strictly for beef purposes . CurrentA herd was defined as the group of animals present in a unique breeding location or farm. Many farms take part in the traditional breeding programs, owning herds composed of their own lineages or a mix composed of their own and external lineages from the whole Brazilian Guzerá livestock. Furthermore, the sampled herds have different selection objectives (selection lines), which were defined here as the group of animals selected for beef, dairy or dual-purpose. Two approaches were applied to understand the structure of this meta-population. In the first approach, each farm was considered a subpopulation; in the second approach, each lineage was considered a subpopulation. The comparison of genetic diversity among different lineages or farms was used to infer the population structure and gene flow in subpopulations.Genomic DNA extraction, PCR amplification, and capillary electrophoresis were developed as described previously . The micAllele frequencies were extrapolated from direct counting. The number of alleles present in the population, numbers of individuals genotyped, expected (He) and observed heterozygosities (Ho), Polymorphism Information Content (PIC), and non-exclusion probabilities were calculated for each locus using the Cervus 3.0  softwareThe Hardy–Weinberg equilibrium (HWE) was tested to support the assessment of the degree of allelic and genotypic diversity in the population. Departures from the HWE could indicate the impact of artificial selection (due to non-random mating) or reduced population size. Heterozygote deficiency was accessed using a multi-sample version of the Score test or U test with the GenePop 4.0 software . The mulis), fixation index (Fst), overall fixation index (Fit)) were calculated following the variance-based method of Weir and Cockerham (1984) [is and pairwise Fst values were calculated for each subpopulation using the Molkin v3.0 software [To estimate genetic differentiation and relationships among subpopulations, Wright (1978)  allele fm (1984) , using tm (1984) . Differesoftware . The AMOsoftware .n = 1000) was performed to evaluate the robustness of the clusters. Additionally, a Neighbor-Net graph , based on the Fst estimated for lineages as subpopulations, was constructed using the SplitsTree4 [Pair-wise standard genetic distances (D)  were calitsTree4  program.The program structure was used to obtain the cluster assignment of lineages through a Bayesian approach, implementing the Markov Chain method with increasing numbers of inferred populations . The tesThe contribution of each lineage to meta-population genetic diversity was assessed using the method proposed by Caballero and Toro (2002)  with theThe microsatellite panel was evaluated in terms of the number of alleles, He, Ho, PIC, and non-exclusion probabilities, presented in The mean observed heterozygosity ranged from 0.47 to 0.85, diverging numerically from the expected values for some loci, mainly for DIK4513. The average Ho was 0.73, slightly below expected (0.77). Such differences are further discussed in light of the HWE tests. Polymorphic information content (PIC) ranged from 0.57 (BM1237) to 0.88 (DIK5183). Combined with the very small non-exclusion probabilities , these rp < 0.05) results were obtained from the HWE test for each locus  between herds [ntiation . These vis was slightly lower (−0.007) and the Fst was slightly higher (+0.007) than those for farms. The Fst for the lineage approach was 0.034, compared to 0.027 for subpopulations grouped according to farm , FARM2 (LINE6), FARM3 (LINE10), FARM4 (LINE1), and FARM5 (LINE12).Estimated values for Fis, Fst, and genetic distances.Farms selecting for dual-purpose were originally beef farms or newly formed. For example, FARM11 (LINE9) was also closed for many years and, only from the end of the 20th century, started to use bulls of different lineages. On the contrary, FARM7 and FARM15 were recently formed by animals of different lineages. In the dual-purpose selection group, the correspondence between farms and lineages was far less strict. These historical aspects affected both the genetic diversity, FFor example, some farms  showed lower levels of heterozygosis, probably resulting from the intensive or exclusive use of their own sires or a small number of sires from few origins. At the same time, other farms  showed high heterozygosis, attributable to the broad use of planned mating and/or semen from animals of different origins or lineages.is using the lineage approach ranged from −0.109 to 0.061. This indicates that genetic diversity also varied among lineages. Some lineages were developed as part of the selection strategy of farms. In those cases, higher Fis were observed for both the farms and the lineages developed within them. For example, some lineages  showed higher Fis and lower heterozygosity, probably resulting again from the intensive or exclusive use of their own sires or a small number of sires from few origins.Estimated values for FOther lineages  showed high heterozygosity. LINE8 originated from LINE7, hence the high genetic similarity between them, and it has also been composed of other lineages resulting in its high heterozygosity. LINE14 came from FARM15, which contains a herd recently formed from a large genetic basis, hence its high degree of heterozygosity.st estimates are shown in st ranged from 0.0029 (FARM15 × FARM1) to 0.048 (FARM6 × FARM3) using the farm approach. They ranged more widely, from 0.007 (LINE8 × LINE11) to 0.087 (LINE7 × LINE10), using the lineage approach. In general, higher average Fst values were estimated for beef lineages. This means that less gene flow has occurred (pairwise Fst), both in the meta-population and in the whole (average Fst). This contrasts with the higher gene flow among dual-purpose lineages expressed by the lower average Fst estimates. One exception can be observed regarding LINE6 (FARM2), which has the lowest pairwise Fst average among the beef lineages (0.018 in the lineage and 0.015 in the farm approaches). Guzerá beef cattle are more extensively bred, generally using little artificial insemination and thus less exchange of genetic material between herds or lineages. However, considering the results of this study, beef herds kept a larger amount of the genetic diversity in Guzerá breeds. The pairwise Fst estimates for the dual-purpose selection lines reveal high genetic variability. This is probably a result of the wide genetic basis for these subpopulations. One clear exception was observed among the dual-purpose selection lines: the average Fst estimated for LINE7 was 0.052, the highest value among all the subpopulations considering dairy, dual-purpose, and beef selection lines. This subpopulation was closed for many generations, and this Fst value reinforces the available information on the breeding policy for this lineage [In general, the lower average F lineage . It shouNei’s standard genetic distances ranged from 0.05  to 0.031  for the farm grouping approach a. In theIn the dendrogram, LINE4, LINE6, and LINE11 were clustered to the same branch. Indeed, LINE11 was derived mainly from LINE4, LINE8, and LINE6 b. In addst values obtained using the farm (0.025) and lineage (0.034) subpopulations were lower than almost all the results shown in The contribution of each subpopulation to the overall genetic diversity is presented in An interesting result was obtained with respect to meta-population structure . If lineDespite the historical events that the Guzerá breed went through in the last decades, such as a strong founder effect and several population bottlenecks, the breed nevertheless preserved a good amount of genetic diversity. In the cluster analysis, it was possible to recover both the origin of the lineages and the selection purposes. Reasonably, in the best model (K = 3), dual-purpose selection lines clustered with the beef selection lines from which they originated. Also, gene flow between the different lineages was detected in the clusters formed. Modeling the genetic structure of the Guzerá meta-population helped to understand the impact of planned crosses on the genetic diversity and the distribution of the genetic diversity among lineages selected for different purposes . The separation of animals for a new selection purpose, if not implying a stronger selection pressure, decreases the risk of loss of diversity. Taken together, these results suggest that, despite the population structure produced by selection strategies based originally on relatively closed farms/lineages, the amount of genetic diversity conserved in the Guzerá breed is high. In particular, beef farms/lineages emerge as a reservoir for both genetic selection and conservation programs.


Shandong indigenous pig breeds are important Chinese pig resources. Their progressive population decline in recent decades has attracted attention towards their conservation. Conservation genetics of these indigenous breeds are essential for developing a conservation and utilization scheme.A high-density single nucleotide polymorphism (HD-SNP) chip-based comparative analysis of genetic characteristics was performed for seven Shandong indigenous pig breeds in the context of five Western commercial breeds.FST] value of 0.3226 and 0.2666, respectively). Contrastingly, the other five breeds  displayed high genetic diversity within breed and had some extent of mixture pattern with the Western commercial breeds, especially Duroc and Landrace . Furthermore, intensive gene flow was discovered among the seven Shandong indigenous breeds, particularly Wulian, Licha, and Heigai, as indicated by the large cluster formed in the principal component analysis scatterplot and small population differentiation (average of 0.1253) among them.The results showed that Shandong indigenous pig breeds varied greatly in genetic diversity, effective population size, inbreeding level, and genetic distance with the Western commercial breeds. Specifically, Laiwu and Dapulian displayed low genetic diversity, and had a genetically distant relationship with the Western commercial breeds (average F statistics [Our study advances the understanding of genetic characteristics of Shandong indigenous breeds and provides essential information for developing an appropriate conservation and utilization scheme for these breeds. Globally, China is the largest pork producing and consuming nation , with ShNe), and inbreeding level. For instance, the Ne and inbreeding coefficient are of major interest in conservation genetics, and common estimation methods based on pedigree information are sometimes not feasible due to the frequent unavailability of pedigree information in the indigenous breeds. High-throughput genotyping technologies allow the study of linkage disequilibrium (LD), and runs of homozygosity (ROH) [Ne and inbreeding coefficient. These technologies have been used to study other indigenous pigs [Over the last few decades, more attention was paid to growth performance and lean meat content due to changes in consumer preference. Resultantly, Western commercial pig breeds  were introduced in China to increase productivity for these traits. This led to the progressive replacement and marginalization of Chinese indigenous pig breeds, some of which are currently close to extinction due to dwindling population numbers or hybridization with highly productive breeds . Populatty (ROH) –17 whichous pigs –20, and ous pigs . Use of Here, HD-SNP BeadChips were employed to investigate the genetic characteristics of seven indigenous pig breeds from Shandong province, China, in the context of widely used Western commercial breeds. First, parameters for the genetic diversity and inbreeding within breeds were calculated. Second, the LD patterns and Ne were analyzed. Finally, individual clustering based on principal components (PCs) and historical admixture patterns were assessed to evaluate the structures among the analysed breeds, and genetic differentiation and phylogenetic trees were conducted to assess the relationship among them. All these joint analyses would provide valuable information for developing conservation and utilization strategies.All animal procedures used were reviewed and approved by the Institutional Animal Care and Use Committee of Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences under permit number IACC20060101.A total of 397 pigs from seven indigenous breeds  and five Western commercial breeds  were used in the study. The sample sizes of each analysed breed are presented in http://www.kangpusen.com/), and manufactured by Illumina . Using PLINK v1.90 [https://figshare.com/) with DOI:10.6084/m9.figshare.12765461 for ped file and 10.6084/m9.figshare.12765656 for map file.All animals were genotyped using Porcine SNP55K Bead Chips according to the manufacturer’s protocol. The BeadChip was designed and promoted by Compass Biotechnology Corporation, Beijing, China , expected heterozygosity (HE), IBS distance (DST). Averages of MAF, HE, HO, and genetic distance  per breed were calculated.The following parameters of genetic diversity were calculated for each SNP and each breed using PLINK v1.90 : MAF, obFROH) was calculated for each individual as described by McQuillan et al [FROH = ∑LROH/LAUTO, where ∑LROH refers to the total length of ROH and LAUTO is the length of the autosomal genome covered by SNPs.The ROHs were identified using autosomal SNPs passing the quality control and PLINK v1.09 . We refean et al , i.e. FRr2) between two SNPs for all marker pairs less than 5Mb for each breed independently using PLINK v1.09 [r2 was then averaged for each group. Average r2 values for each breed were plotted against physical distance using the R program.The LD was determined using the squared correlation coefficient ×(1/r2–1) [r2 is the LD of different distances. The average high-density recombination rate reported by Tortereau et al [Ne. The current Ne was calculated using the mean value of r2 for all 1 Mb bins across the entire genome as described by Herrero-Medrano [Ne at generation T, where T = 1/2c, was estimated using the above equation [(1/r2–1) , where cau et al  was cons-Medrano , whereasequation .FST) values were estimated using PLINK v1.90 [Principal component analysis (PCA) was performed using PLINK v1.09 , and theNK v1.90  based onNK v1.90 . AdditioNK v1.90 , rates aNK v1.90  using MENK v1.90 .After quality control, 39,300 were remained out of the 43,832 SNPs contained in the BeadChip. A summary of the SNP distribution and average distance of the adjacent SNPs on every chromosome are shown in HO, HE, and D values of 0.1621, 0.2375, 0.2252 and 0.1855, respectively. In contrast, Yantai, Licha, and Wulian exhibited the highest genetic diversity, with Yantai displaying the highest MAF (0.2901) and HE (0.3750), while Licha and Wulian the highest HO (0.3837) and D (0.2981), respectively. Comparatively, all Shandong indigenous breeds, except Laiwu and Dapulian, displayed higher genetic diversity than the five Western commercial breeds.The genetic diversity parameters estimated for each breed are presented in FROH (0.0527). Contrastingly, Duroc had the highest number of ROH (74.13), longest total length of ROH , and highest FROH (0.2783). Western commercial breeds had higher FROH than Shandong indigenous breeds (average 0.2134 vs 0.1312), while Shandong indigenous breeds had larger standard deviation than Western breeds (average 0.0732 vs 0.0441). Thus, our results show that Shandong indigenous breeds have lower averaged breeding levels. However, Shandong indigenous breeds have higher inbreeding variation within breeds compared to Western breeds.ROHs for the analysed populations were estimated to de termine the inbreeding levels. As shown in r2 was estimated for all SNP pairs in a distance up to 5 Mb apart across the genome, and the levels of LD at different distances for each breed are provided in r2 than Western commercial breeds. The averaged r2 values were 0.32, 0.23, and 0.17 for adjacent SNPs, SNPs 1 Mb apart, SNPs 5 Mb apart for Shandong indigenous breeds, and 0.44, 0.30, and 0.19 for Western commercial breeds, respectively.To assess LD patterns, r2 with distance, i.e., it decreased rapidly over the first 0.2 Mb distance, and then decreased gradually over the remaining 0.8 Mb distance. The LD of Landrace decreased by the half at 0.25 Mb, showing the highest LD decay, while the LD of Laiwu and Dapulian decreased by the half at 0.95 Mb, showing the highest LD persistence. Similar r2 values for all the distances were observed for Licha and Wulian, indicating the genetic closeness of the two breeds.The LD along physical distance between markers was also plotted to visualise the LD along chromosomes, and the plot is presented in Ne was estimated for each breed based on the estimated r2 , followed by Licha (130), while Yimeng had the smallest Ne (83), followed by Laiwu (92). The past Ne was also estimated. As shown in Ne of Shandong indigenous breeds was larger than those of Western commercial breeds, too.The present mated r2 . The preFirstly, PCA was performed to explore the clustering of individuals of different breeds . The resThen, historical admixture patterns of the analysed breeds were assessed using K values from 2 up to 12 with Admixture. The CV errors  decreaseFST values were further estimated to determine the extent of population genetic differentiation, and the results are shown in the lower diagonal of FST values were observed between Shandong indigenous breeds and Western commercial breeds, with an average of 0.21, indicating considerable genetic differentiation between them. Laiwu had the largest genetic differentiation with all the Western commercial breeds (0.2988 to 0.3489), followed by Dapulian (0.2411 to 0.2875). In contrast, FST values among Shandong indigenous pig breeds changed markedly from 0.0441 (Wulian and Licha) to 0.2554 (Laiwu and Yimeng), and most of their values were low with an average of 0.1253. Notably, Laiwu also displayed the highest genetic differentiation with the other Shandong indigenous breeds, especially with Yimeng (0.2554) and Yantai (0.2265).Pairwise FST values. The Western commercial breeds clustered together at the top of the tree, while the seven Shandong breeds clustered together at its bottom. However, the clades formed by Shandong indigenous and Western commercial breeds were not completely separated. Yimeng and Yantai clustered first with Duroc before clustering with the other Shandong indigenous breeds, indicating the close relationship between the two breeds with Western commercial breeds, especially with Duroc.Finally, pair-wise evolutionary distances among the analysed breeds were calculated  value, even higher than most Western commercial breeds. The FROH values of the other Shandong breeds were low to moderate, with Ne above 100 except Yimeng (Ne = 83). Nevertheless, there was significantly large within-breed variation in FROH values for these breeds, indicating the presence of individuals with high inbreeding levels. Such individuals should be specifically considered when planning matings. Furthermore, an effective population size of 100 should be considered to maintain a population’s genetic diversity [Ne values slightly higher than the recommended number, while some are close to the recommended number. Thus, appropriate methods should be applied to control the inbreeding levels and improve Ne to protect these genetic resources.Of the Shandong indigenous breeds, Laiwu presented the highest iversity . Some ofFST values, which are based on allele frequency differences among populations [FST value of 0.21, ranging from 0.1043 to 0.3479. Based on the degree of genetic differentiation using FST thresholds  specified by Hartl and Clark [All population structure and relationship analyses per formed in the present study demonstrated that Shandong indigenous pig breeds were genetically distant from the Western ones. Particularly, ulations , quantitnd Clark , moderatnd Clark ,21.FST values with the Western commercial breeds, with averages of 0.3226 and 0.2666, respectively. Furthermore, Laiwu and Dapulian located at the distal end of the large cluster with slight overlap with other indigenous pigs . They were located close to Western breeds clusters , overlapped in the cluster of PCA (In contrast, the other five Shandong indigenous pig breeds had relatively small genetic differentiation with the Western breeds (clusters  and had clusters . All ther of PCA , and sepr of PCA . This su