Wolbachia endosymbiotic bacteria are found in a diversity of terrestrial arthropods and nematodes, with supergroups A and B the most common in insects. We identified and assembled 110 complete Wolbachia genomes from 93 host species spanning 92 families by filtering data from 368 insect species generated by the DToL project. From 15 infected species, we assembled more than one Wolbachia genome, including cases where individuals carried simultaneous supergroup A and B infections. Different insect orders had distinct patterns of infection, with Lepidopteran hosts mostly infected with supergroup B, while infections in Diptera and Hymenoptera were dominated by A-type Wolbachia. Other than these large-scale order-level associations, host and Wolbachia phylogenies revealed no (or very limited) cophylogeny. This points to the occurrence of frequent host switching events, including between insect orders, in the evolutionary history of the Wolbachia pandemic. While supergroup A and B genomes had distinct GC% and GC skew, and B genomes had a larger core gene set and tended to be longer, it was the abundance of copies of bacteriophage WO who was a strong determinant of Wolbachia genome size. Mining raw genome data generated for reference genome assemblies is a robust way of identifying and analysing cobiont genomes and giving greater ecological context for their hosts.The Darwin Tree of Life (DToL) project aims to sequence all described terrestrial and aquatic eukaryotic species found in Britain and Ireland. Reference genome sequences are generated from single individuals for each target species. In addition to the target genome, sequenced samples often contain genetic material from microbiomes, endosymbionts, parasites, and other cobionts.  Wolbachia are common bacterial endosymbionts that manipulate reproductive biology in their hosts. This study assembles the genomes of 110 Wolbachia coincidentally sequenced alongside their insect hosts, finding a rich diversity of host manipulation loci. Wolbachia  are found living intracellularly in a range of terrestrial arthropods and nematodes. No free-living Wolbachia are known: The association is essential for their survival. In contrast, infection with Wolbachia can be beneficial to hosts but is not usually essential.The natural world is a complex web of interactions between living species. These interactions can be mutualistic, commensal, pathogenic, parasitic, predatory, or inconsequential, but each individual lives alongside a rich diversity of cobionts. Most eukaryotes associate intimately with a specific microbiota and are commonly infected by a range of microbial and other pathogens. For some microbial associates, the distinction between mutualism and pathogenicity or parasitism is fuzzy. For example, Wolbachia were first identified as mosquito endobacteria that were maternally transmitted, through the oocyte, and that induced a range of reproductive manipulations on their hosts Studying the biology of intracellular symbionts like Wolbachia is challenging since they are often not amenable to genetic manipulation. Comparative genomics has been an important tool in identifying the genetic basis of symbiont-induced phenotypes such as reproductive manipulations and in understanding the major evolutionary forces that shape symbiont genomes. To date, a significant proportion of Wolbachia assemblies publicly available are fragmented as they were often generated using short read sequencing technologies which prevents the assembly of large repeat regions that are abundant in Wolbachia. This has been a limitation for the studying the evolution of symbiont gene content and genome architecture. Moreover, available genomes are biased towards certain Wolbachia-carrying host taxa and model organisms, and it is unclear how one can generalize observations such as the incidence of certain Wolbachia clades in nature.The present study by Vancaester & Blaxter generated a large number of high quality and complete to near-complete Wolbachia genomes isolated from a wide diversity of host taxa which is an important step to overcome these challenges. By analyzing these new genomes along with publicly available assemblies, the authors provide general insights into Wolbachia evolutionary genomics such as the role of prophage sequences in genome size variation. Another important finding is that the new genomes presented here are bigger on average than previous Wolbachia assemblies which highlights the need for using high-accuracy long-read sequencing technologies.This new set of genomes will be of high interest to the scientific community and will undoubtedly facilitate more in-depth analyses of Wolbachia evolutionary genomics. I have some comments/suggestions below that I hope the authors will find useful. Great work!1) Line 73: the authors should also refer to the Pascar & Chandler 2018 study (PMID: 30202647) who generated many Wolbachia draft genomes using a similar approach.2) Line 85-86: Does that mean that somatic tissues were selected for sequencing and ovaries generally discarded? Maybe specify. If that is the case, it would be useful to make it clear that some Wolbachia infections in this collection of insects may have been missed since Wolbachia infections can vary greatly in their tissue distribution, some being restricted to germline tissues .3) Line 107-109: this is also in line with the more recent Weinert et al. 2015 study which accounted for sampling bias (PMID: 25904667).4) Lines 114-120: it might be useful to clearly define the difference between prevalence  and incidence (proportion of host species where the symbiont is present in a given host clade).5) Line 146-148: there is evidence in the literature that Wolbachia titer/abundance within hosts can be controlled both by the host and the bacterial genomes. However, for most associations, the genetic determinants of Wolbachia proliferation have not been characterized. Therefore the statement "Most infected hosts tightly control Wolbachia proliferation" is inaccurate/not supported since looking at Wolbachia titers alone does not tell us whether it is controlled by the host, the symbiont or both.6) Line 149: 48 Wolbachia per host "genome".7) Line 259-262: I suspect that a lot of these "novel genes" might be pieces of pseudogenes. The fact that they were found to be much shorter on average and mostly annotated as transposon/mobile elements is not surprising since transposase and reverse transcriptase genes are abundant in Wolbachia genomes and are often highly degraded. On top of this, these mobile elements often insert themselves within and disrupt other genes. As I understand, the genome annotations were not manually curated to reannotate pseudogenes and I wonder how much of these "novel genes" are simply degraded/split/truncated copies of genes that are present in full length in other genomes but that OrthoFinder failed to place in the correct orthogroup, instead clustering them into a one/two member orthogroup. Could the authors elaborate on this and mention in the manuscript whether they think this is a limitation for defining what a novel gene is? I guess this is to be kept in mind when estimating the size of the core/pan-genomes and looking at variation between Wolbachia supergroups .8) Lines 314-315: can the authors explain their rationale for using the 80% threshold to define a prophage region as complete and what they mean by "complete"? If a prophage region carries >80% of genes of each phage module but is lacking an essential structural phage gene preventing it to produce phage particles, should it be called complete? Also, in line 476, defining a phage copy as "active" would mean that there is some evidence the phage is replicating and/or producing phage particles. I was wondering if the authors have any indication on which prophage region might be active, based on variation in sequencing depth. If not, I would probably avoid calling them active.9) Lines 397-399: the balance also depends on loss of infections through time (not only gains).10) Lines 345-351: from the method section, it seems that the authors used a coverage threshold for the detection of cif genes that should miss typical type V cif genes. If I understand correctly, representative cif genes from Type I (cidA/B) and Type IV (cinA/B) were used as queries and only hits that had 80-120% coverage were retained. However, type V cifB genes are typically much longer than type I-IV (4-5x longer) due to the presence of additional domains such as ankyrin repeat and a C-terminal latrotoxin domain . Therefore, my guess is that more full-length type V cif genes are present in this set of new genomes than reported in line 350 (50 type V homologues). For that reason, I also wonder how many of the latrotoxin domain-containing proteins reported in line 352 are in fact full copy or the 3'-end of truncated type V cifB proteins. I would suggest that the authors use representative type V cif genes in addition to type I-IV as queries or instead mention that they might be missing a lot of type V genes in their analysis. Another solution would be to remove the 120% maximum coverage threshold to include the longer cifB genes.11) Table S3: The reference genome accession GCF_001931755.2 was isolated from the Springtail Folsomia candida (Collembolla), not from a coleopteran host as indicated in Table S3.Reviewer #2:Wolbachia is the most successful host-associated microbe on the planet, estimated to infect ~40% of all terrestrial arthropod species. These symbionts are transmitted primarily from mothers to their offspring, and as a result, have evolved diverse strategies to increase the fitness of infected female hosts. There is also a great deal of applied interest in using various strains of Wolbachia to control insect pests and vectors of disease. This study takes advantage of the Darwin Tree of Life project, which aims to sequence the genomes of all eukaryotes in Britain and Ireland. Because Wolbachia is so pervasive, the researchers have been able to assemble and analyze over 100 high quality Wolbachia genomes, from symbionts infecting the first insects to have been sequenced in this project, mostly from Wyntham Woods near Oxford. This allows for the most comprehensive comparative genomic study to date of the 2 major Wolbachia supergroups (A and B) that infect insects, and a resource that will be heavily used by the Wolbachia and microbial symbiont community. There are many interesting findings, including clear demonstration that A and B group Wolbachia are distinct. A major question is how these two strains coexist in insects, and the authors find one interesting difference that may help answer this question, with A (but not) B group members containing an operon for arginine transport, suggesting that they take this nutrient from their hosts. Another interesting finding relates to biotin synthesis. Only a small fraction of Wolbachia harbor the biotin synthesis pathway, repeatedly acquiring this via horizontal gene transfer, and there has been speculation that providing biotin to hosts may be an important factor in establishment and persistence of some Wolbachia. Interestingly, the authors find that the biotin operon is strongly associated with toxin-antitoxin and selfish element genes, pointing to an interesting connection between selfish and 'mutualistic' Wolbachia genes and functions that merits further study.In my opinion, this paper represents a very useful contribution to the Wolbachia and microbial symbiont field, and demonstrates the great potential of using high-quality eukaryote genome sequences to study their microbial infections. Also, the paper is beautifully written, and I found the figures to be of especially high quality. I don't have many comments or suggestions to strengthen the manuscript. Although the paper is very well-written and easy to read, I didn't find the discussion added very much new that wasn't already mentioned in the results or introduction. I would have also been interested to see more information about Wolbachia toxin evolution diversity, including some more phylogenies. The authors report the presence of spaid-like toxins, and it would be useful to get some more information about these. The spaid toxin was recently found to cause male-killing in Spiroplasma bacteria, so it would be interesting to understand what related genes are doing in Wolbachia . Finally, it would be interesting to learn more about the distribution and diversity of wmk genes, as these have been recently implicated in male-killing by Wolbachia. A recent study in biorxiv by Arai et al. found an interesting connection between male-killing and wmk copy number, and the high quality long-read sequence data presented in the current study has great potential to illuminate on this. Reviewer #3:This manuscript reports 110 high quality new genome sequences of the bacterial symbiont Wolbachia. This resource is a major advance for the field of Wolbachia research. Not only does it roughly double the number of available genomes, but these genomes are of better quality and benefit from less biased sampling than those that are available already. There is a great deal of interest in the biology of this symbiont at the moment, with advances in understanding the genetics of its interaction with insect hosts and its deployment to control mosquito-borne viruses. As Wolbachia cannot be cultured outside of cells, research frequently relies on comparative studies. In the long term, the greatest contribution of this paper will likely be as a resource to this research community. As well as reporting the genomes themselves, the manuscript begins by describing the distribution of Wolbachia across host species and compares the host and symbiont phylogenies. There is already a substantial literature on these topics and this analysis does not lead to significant new insights. Nonetheless, having these patterns confirmed using whole-genome data is reassuring.The remainder of the manuscript describes the properties of the genomes, and the richness of the data allows many new patterns or more complete patterns to be reported. For example, rearrangements within genomes, genetic exchange between genomes and gene content. The distribution of genes with a clear function, such a biotin synthesis, provides fascinating insights into the biology of Wolbachia. These results will be of great interest to the field.I have a few minor suggestions that the authors may consider, but in my judgement none of them are necessary for publication.Line 183-192. This is unsurprising given the literature on this topic. This data, like other datasets, shows that Wolbachia and host trees show many incongruences. However, it is also clear they are not independent. It seems that at the least this should be noted as a counterpoint to the observation of incongruences (eg a Mantel test comparing genetic distances of hosts and symbionts). In the future, this may be a nice resource to understand what predicts which species Wolbachia jumps between.Figure 2C. This plot is most effective at showing synteny but it is used to demonstrate a recent host shift of Wolbachia between insect orders. There needs to be a clearer explanation of why this is the best way to show this - a tree seems more straightforward. I guess the answer is likely in Figure 3C, but this comes after.Figure 3A. Is ANI just coding sequence? State In legend. The description of this in the text seems a bit odd as the correlation of sequence and structural divergence seems somewhat inevitable - maybe commenting on the degree of rearrangements might be more useful? It looks like synteny is pretty low except between very closely related strains.Line 218-231. The definition of GC skew/skewI needs a clearer explanation. Explain why it is plotted against GC content. The interpretation of this statistic is a bit unclear. It is stated that groups A and B have low skew. Is this just relative to other supergroups, or bacteria in general? If the latter, does this translate directly into a measure of the rate of genome rearrangement.Line 352-362. The distribution of different toxin genes is interesting but a bit hard to follow. A supplementary table or figure would make it more digestable. Line 406 'less likely' needs some justification. Line 412 seems to suggest ecological effects matter, and if there is preferential switching between hosts with shared ecology whis wil generate phylogenetic clustering. Its also unclear how 'host genetics' and 'wolbachia' genetics differ in this list - presumably you mean the interaction of the two.Line 423-426. This seems like a very important pattern, but the text here does not seem to flow clearly though. Line 427. I guess you mean 'female hosts'. The rest of this paragraph could do with a few citations of similar work.Are there any plasmids?Fig S5. What is the statistical test? 15 Dec 2022AttachmentReviewerReport_91222.pdfSubmitted filename: Click here for additional data file. 19 Dec 2022Dear Dr Vancaester,Thank you for the submission of your revised Methods and Resources "Phylogenomic analysis of Wolbachia genomes from the Darwin Tree of Life biodiversity genomics project." for publication in PLOS Biology. On behalf of my colleagues and the Academic Editor, Luis Teixeira, I'm pleased to say that we can in principle accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.Please take a minute to log into Editorial Manager at biologypress@plos.org. If you have previously opted in to the early version process, we ask that you notify us immediately of any press plans so that we may opt out on your behalf.PRESS: We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with http://www.plos.org/about/media-inquiries/embargo-policy/.We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study. Sincerely, Roli RobertsRoland G Roberts, PhD, PhDSenior EditorPLOS Biologyrroberts@plos.org


Lupinus mutabilis as a protein and oil crop requires improved varieties adapted to EU climates. The genetic regulation of strategic breeding traits, including plant architecture, growing cycle length and yield, is unknown. This study aimed to identify associations between 16 669 single nucleotide polymorphisms (SNPs) and 9 agronomic traits on a panel of 223 L. mutabilis accessions, grown in four environments, by applying a genome wide association study (GWAS). Seven environment-specific QTLs linked to vegetative yield, plant height, pods number and flowering time, were identified as major effect QTLs, being able to capture 6 to 20% of the phenotypic variation observed in these traits. Furthermore, two QTLs across environments were identified for flowering time on chromosome 8. The genes FAF, GAMYB and LNK, regulating major pathways involved in flowering and growth habit, as well as GA30X1, BIM1, Dr1, HDA15, HAT3, interacting with these pathways in response to hormonal and environmental cues, were prosed as candidate genes. These results are pivotal to accelerate the development of L. mutabilis varieties adapted to European cropping conditions by using marker-assisted selection (MAS), as well as to provide a framework for further functional studies on plant development and phenology in this species.Establishing  Lupinus mutabilis, also known as “Andean lupin”, is an endemic legume of the Andean region of South America. Firstly domesticated in the north of Peru , which exceeds that of any other lupin species and is comparable to soybean. On top of that, L. mutabilis is adapted to low input farming in temperate climates and can effectively contribute to the improvement of poor soils by fixing nitrogen and mobilizing soil phosphates. In Europe and other temperate climatic regions, the combination of these features makes L. mutabilis a potentially superior alternative to current plant-based sources of protein and oil. Despite this, some challenges need to be addressed to expand its cultivation on large scale, as L. mutabilis remains to date an under studied crop. Pivotal to achieving this aim are breeding programs focused on guaranteeing economic viability and consumer acceptance of the crop. of Peru , L. mutaood crop . Similarood crop . Only inL. mutabilis seeds in a wide range of end-use products, from protein, oil and food additives to cosmetics, medicines and bio-pesticides. In contrast, few studies have addressed the agronomic aspect of its cultivation. Research on the adaptation of L. mutabilis to European soil and climate conditions started only 30 years ago, when the first European project focusing on 16 selected lines was initiated. More recently, a second European project (LIBBIO) has expanded this investigation to 225 L. mutabilis accessions, by evaluating a wide panel of Andean germplasm both as a winter crop in Mediterranean conditions and as a summer crop in North-Central European conditions. Both projects have pointed out the need of breeding for a better plant architecture, early maturity and yield stability -like gene controlling transition to flowering has led to determinate growth habit in many legumes, including pea , controlling the developmental transition from indeterminate to determinate growth habit  and its switch to determinate growth can be considered as one of the most important traits of their domestication . The ideding pea , faba beding pea , common ding pea  and soybding pea . TFL1 beth habit . Besidespathways . Within e growth . NeverthGiven the importance of these agronomic traits, a large number of genomic regions  associated to plant-architecture related traits, flowering time and seed yield has been identified in the past decades for many grain legumes . These sL. mutabilis in the native Andean region, and over two cropping conditions in Europe, in order to identify Single Nucleotide polymorphisms (SNPs) underlying the variation in plant-architecture, flowering and yield related traits in this species. A GWAS approach was used to capture the natural diversity present in the panel, with the objective to develop genetic markers and highlight genomic regions (QTLs) harboring causal candidate genes, which are critical for assisting and speeding up the breeding of L. mutabilis.In this study, we evaluated a panel of 223 diverse accessions of L. mutabilis accessions, 201 provided by the Instituto Nacional de Investigaciones Agropecuarias of Quito , comprising landraces, varieties and wild material collected across the Andean region, and 22 L. mutabilis lines developed in European breeding programs. The panel was evaluated in a total of four field trials in Ecuador and Europe during the growing seasons 2019 and 2020. The locations were chosen to represent an example of cultivation in the native environment as well as in Mediterranean (winter cycle) and North-Central European (summer cycle) climates and photoperiod regimes. The trial in Ecuador  was sown in December 2019 and harvested in June 2020. The field layout was an alpha-lattice design with 3 replicates. Plants were arranged in plots with 5 rows (80 cm between rows), containing 40 plants spaced 20 cm. In Europe, the field trials were set up during winter (Nov 2019 – May 2020) for the Portuguese site , and during two consecutive growing seasons from April to October in two locations in The Netherlands . A randomized complete block design (RCBD) with three replicates was adopted for the three EU trials. Out of 20 plants present in each plot, at a distance of 30x30 cm, phenotyping was conducted on the 6 central plants. In all the locations, plants were cultivated under rain-fed conditions and without the aid of any fertilization, following local cultivation practices.The GWAS panel used in this study comprised 223 L. mutabilis GWAS panel across the four environments included the scoring of quantitative traits related to plant morphology, phenology and agronomic performance , to correct for spatial gradients in the field by adopting a 2-dimensional smoothing with P-splines , environment (E) and genotype by environment interaction (GEI) and estimates of broad sense heritability across trials. Broad sense heritability was calculated across the three environments as in .where L. mutabilis leaves  using acetyl trimethyl ammonium bromide (CTAB) method (L. angustifolius ‘Tanjil’ (LupAngTanjil_v1.0 refSeq GCF_001865875.1) genome reference (Reduced representation sequencing and single nucleotide polymorphism (SNP) typing was performed as previously described in ) method  as descreference . The ave10 (p) that was used to state statistically significant SNPs. Manhattan plots were visualized using qqman package in R < 0.02. The GWAS model was based on a linear mixed model for association mapping as implemented in StatgenGWAS package v1.0.5 . Single age in R . Linkageage in R . LD was Lupinus angustifolius Annotation Release 100 for the genome assembly GCF_001865875.1 of LupAngTanjil_v1.0 . Special attention was given to genes with predicted functions related to the flowering time and regulation of plant growth and development, based on gene description and relevant research paper.The size of the genomic regions investigated to identify putative candidate genes, controlling the traits under study, was defined by the extend of the average Linkage Disequilibrium across the genome. Starting from the position of the detected significant SNPs, candidate genes were proposed whether harbored in a maximum physical distance, upstream and downstream, of 80 kbp. All candidate genes were selected based on the information contained in the NCBI L. mutabilis accessions adapted to different cropping conditions in Europe and Ecuador were investigated using a panel of 223 accessions. Extensive phenotypic variations were observed for all traits, namely flowering time, plant height, number of branching orders, vegetative yield, number of pods and seeds on the main stem, total number of pods and seeds and seed weight  and EC (73 cm) compared to NL-Sc (90 cm) and NL-Wi (92 cm) and characterized on average by a first branching order only. Contrarily, in NL-Sc and NL-Wi the main stem reached an average height of 90 and 92 cm respectively, with two/three branching orders produced. Plant height had high phenotypic variability within the 223 accessions, as pointed by the CV values of 17%, 18%, 20%, 13% in Ecuador, Portugal, NL-Sc and NL-Wi respectively. Vegetative yield reflected the development of L. mutabilis in different environments. In Portugal, the average vegetative yield recorded (45.8 g/plant) was between three and four times lower than the average yield of NL-Sc (133.6 g/plant) and NL-Wi (174.9 g/plant). Concerning grain yield components, the average number of pods and seeds produced by the total plant, as well as by the main stem only, was higher in winter-Mediterranean conditions, followed by the native environment (EC) and summer-North European environments. The number of seeds harvested per plant was on average 42 in EC (CV 47%), 95 in PT (CV 19%), 26 in NL-Sc (CV 49%) and 42 (CV 53%) in NL-Wi. The number of pods and seeds produced on the main stem were respectively 9 (CV 20%) and 25 (CV 28%) in EC, 13 (CV 23%) and 42 (CV 21%) in PT, 3 (CV 89%) and 14 (CV 47%) in NL-Sc and 9 (CV 27%) and 20 (CV 37%) in NL-Wi. Conversely, the yield of biomass agricultural residues was lowest in PT (45.8 g/plant) and highest in NL-Wi (174.9 g/plant). An exhaustive analysis of the phenotypic traits under study was conducted in a previous study of diversity and agronomic adaptation of this collection. There, the mean values, range of variation and Pearson’s correlation between plant architecture and yield-related traits are reported  and plant height (0.5-0.8). Moderate to high heritability was also estimated for number of pods on the main stem (0.4-0.8), number of seeds on the main stem (0.4-0.8), total number of pods (0.2-0.6), total number of seeds (0.2- 0.7), 100 seeds weight (0.1-0.67), and moderate to low value for vegetative yield (0.1-0.5) and branching order (0.1-0.4). When looking at the data across locations (averaged data), the analysis of variance indicated that genotype (G) and environment (E) had a significant effect on all the traits analyzed (P< 0.001). The effect of genotype by environment interaction (GEI) was also highly significant (P< 0.001) for the majority of the traits but had no significant effect on vegetative yield. As shown in The GWAS analysis was performed using the R package StatGenGWAS. The Kinship relationship matrix among samples, calculated with the Van Raden method, was included together with the country of origin of the accessions as covariate, to adjust for population structure. The quantile distribution of the observed p-values versus the expected p-values (QQ plot) confirmed adequacy of the population structure correction.10 (p) > 5.52), a total of 5 significant SNPs were identified as associated with flowering time, 1 SNP was associated with plant height, 1 SNP with vegetative yield and 4 SNPs with the number of pods on the main stem (Pods MS) . A gene encoding an E3-ubiquitin protein ligase RING1 was detected on chromosome 17 in linkage with the SNPs M14675, that showed significant association with Pods MS. Members of the E3 ubiquitin ligases are well known to play a role in plant growth and development. Additionally, important genes involved in plant growth were identified on chromosome 12 and 16. Genetic polymorphism in these genes may be associated with plant height and vegetative yield and captured by blocks of closely located markers: M12928 to M12945 on chromosome 16 and M10488, M10515 and M10527 on chromosome 12 (RF2b), shade-induced plant growth (HAT3), transcriptional regulators of flowering time  and genes involved in the gibberellin signaling pathway (GA3OX1).We searched for putative candidate genes in regions close to the peak of the eight QTLs detected. An interval of ± 80 kbp around the peak of these QTLs was taken as search window for candidate genes as this is about the size of the linkage disequilibrium blocks, hence genes outside this window are not likely to be causative for the QTL effect. All search regions together harbored a total of 91 unique genes on the osome 12 Figure 4L. mutabilis accessions studied, indicating that the used panel is suitable for a GWAS study that requires genetic variation of the traits. The large phenotypic variation observed in our population is beneficial to select breeding candidates in order to initiate a genetic improvement program aimed at developing superior varieties adapted to Europe and high performing. One of the key factors to promote the cultivation of high yielding L. mutabilis in Europe is breeding towards earliness and semi-determinate growth habit , we demonstrated that some phenotypic traits of interest are highly influenced by their genetic components. We report high heritability values for flowering (H2 = 0.88), plant height (H2 = 0.82) and production of pods and seeds on the main stem (H2 = 0.6) across the different trials, thus across environments and growing conditions , and vegetative yield, to potentially breed for novel early and high yielding lines. The highlighted variation is therefore suitable to support breeding aimed at the introduction of nditions Table 1.nditions . These fL. mutabilis. Since genotype by environment interaction can alter the QTLs effects, an initial GWAS analysis carried out on single trial data, followed by a genome-wide scan across all locations, was considered the best approach to analyze our data. We have already shown in previous studies that in the absence of a reference genome for L. mutabilis, the use of L. angustifolius pseudochromosomes assembly can be suitable for genome-wide associations in this species  investigating the genetic architecture of agronomic traits in a collection of  species . In addi species . The assP-value , 1 SNP associated with plant height , 1 SNP associated with vegetative yield , and 3 SNPs associated with number of pods on the main stem . In agreement with the idea that the genetic control of agronomic traits might differ in response to different environmental conditions, our study points out that the association of genomic regions with phenotypic traits can be location specific, thus different QTLs can be detected in different environments. Indeed, most of the SNPs reported were identified as single location markers. Taking into account that the Bonferroni correction method is a very conservative statistics to set a threshold P-value , we haveL. albus, where it has been described as quantitative and under the control of major QTLs and numerous regulatory genes, including FLOWERING LOCUS T (Fta1), CONSTANS (CO), FY, MOTHER OF FT AND TERMINAL FLOWERING LOCUS 1 (TFL1), but also of genes related to the response to photoperiod and vernalization, as well as regions involved in hormone signaling pathways  (FANTASTIC FOUR 3 (FAF3) at a distance between 65 kb and 77 kb from M7413, M7412 and M7399, thus located in a linkage disequilibrium block captured by these three proximal markers. FAF3 is a member of the FANTASTIC FOUR (FAF) genes family, which contains four members in Arabidopsis (FAF1-FAF4), dynamically expressed throughout development -CLAVATA (CLV3) signaling pathway that plays a central role in regulating stem cell proliferation and differentiation in crop plants, supporting organogenesis in the floral meristem through a complex molecular pathway  at a distance of 62.3 kbp. GAMYB is described as a regulator of flower induction, via the transcriptional activation of the LEAFY (LFY) gene (NIGHTLIGHT–INDUCIBLE AND CLOCK-REGULATED (LNK1 and LNK2) genes are known to control photomorphogenic and photoperiodic responses, as well as circadian rhythms  . In our elopment , and mai pathway . Notablyparalogs  that couFY) gene , that reFY) gene . Instead rhythms . Recentle needed . DespiteRF2b-like and a homeobox-leucine zipper protein HAT3, both harbored in the genomic regions where we visualize an interesting piling up of SNPs, that likely resulted as false negative when applying Bonferroni threshold to detect significant associations. These genes are respectively involved in vascular development and shoot tissue organization (RF2b) and in shade-induced plant growth (HAT3), and can both lead, through different mechanisms, to the development of semi-dwarf varieties  in Ecuador, but a piling up of SNP (not significant based on Bonferroni) was instead observed on chromosome 16 in Portugal, and on chromosome 12 in the Netherlands. On chromosome 1 we did not report any relevant candidate genes linked to M396 and plant height. Contrarily, we report the finding of a transcription factor arieties .GIBERELLIN 3-BETA-DIOXYGENASE1 (GA30X1), HISTONE-DEACETYLASE15 (HDA15), transcription factor BIM1 and Dr1 homolog, which are gene involved in the gibberellin pathway (GA30X1) and regulation of flowering time . On one hand, GA30X1 is involved in the gibberellin pathways as the enzyme catalyzing the final step of the synthesis of bioactive gibberellin during vegetative growth, and its negative regulation might directly lead to substantial decreases in biomass production and to the development of semidwarf types  and consequent floral repression, promoting in turn vegetative growth. Conversely, defects in BIM1 can lead to early floral transition . This mechanism has been described as a successful drought escape strategy in wheat  was detected. M10925 showed a strong association, and it is able to explain a large share (~20%) of the phenotypic variation. This suggest the presence of a major QTL, considering that vegetative yield is a quantitative and complex trait. Harbored in this area, we identified 4 different candidate genes: rf types . On the ansition . Insteadperature . For exain wheat , and couRING1 gene in linkage disequilibrium with the respective significant marker. E3 ubiquitin ligases have been documented to play an important role in the regulation of plant growth and development, such as seed dormancy and germination, root growth and flowering time control, as well as regulation of several abiotic stress responses . One SNP was detected across trials , while the remaining three SNPs indicated QTLs location-specific. M11034  was detected in the NL-Sc, M1483  in EC and M14675  in NL-Wi. On chromosome 17, we highlighted the presence of the E3 ubiquitin ligases esponses . IncreasL. mutabilis breeding in Europe can beneficiate from the findings of this study as alleles for the QTLs can be typed and potentially used to assist the genetic selection. Furthermore, the highlighted association of genomic regions with the reported phenotypic traits represents a molecular framework for further deeper investigations of the intricate networks regulating plant development and phenology in this species.http://doi.org/10.4121/21334146.v1).The genotypic data used for the genome wide association study and generated by genotyping by restriction site-associated DNA sequencing (RAD-seq) are openly available at the 4TU. Research Data repository (AG and LT conceived and designed the experiment. AG performed all the analysis, interpreted the data and wrote the manuscript. AL contributed to the interpretation of the data and the writing of the manuscript. LT wrote the proposal and revised the manuscript. M-JP and EL provided guidance in the statistical analysis and genome-wide association study and revised the manuscript. All authors read and approved the manuscript.


The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals. Fetal programming describes the series of fetal responses to in utero stimuli, including the maternal genetic and metabolic milieu, during critical phases of cellular differentiation that may substantially impact fetal development . AccordiTo provide a unified framework for more fully interrogating multi-omics fetal programming contributions that underlie maternal–offspring phenotype associations, we adapt a Bayesian network modeling approach. A Bayesian network is a graphical probabilistic model defined from a set of variables and their conditional dependencies via a directed acyclic graph (DAG). This analytic strategy has been successfully deployed for other analyses of omics data ,14,15,16Interpretation and utilization of this scale of integrated omics analysis demands sophisticated graphical display that is accessible to multidisciplinary teams of investigators. To complement Bayesian network estimation, we apply visualization techniques that demonstrate network growth over a range of penalty parameters for the model. In so doing, we observe the varied strengths of multi-omic associations with the HAPO maternal–offspring phenotypes of interest. The approach to comprehensive network visualization is complemented by the ability to highlight specific ordered paths of interest when undertaking strategic analysis of potential mediation mechanisms.p < 10−7.5 in per-metabolite GWAS analyses. Forty-three pairs of maternal and newborn SNPs remained after LD trimming (APOA5 (rs2072560 and rs2266788) with fasting maternal triglycerides, GCKR with fasting and 1-hr maternal 2-hydroxybutyric acid and maternal 1-hr lactate, and SLC16A9  with fasting and 1-hr maternal AC C2 (CPS1 (rs1047891 and another SNP 2500 base-pairs away) with fasting and 1-hr maternal glycine. Three newborn SNPs  in the ACADL region and one newborn SNP (rs3738934) in the RPE region were highly associated with cord blood AC C8:1-OH/C6:1-DC. Forty-nine newborn SNPs in the REV3L region were highly associated with cord blood NEFA. The 43 pairs of maternal and newborn SNPs listed in Across 5.2 million genotyped and imputed SNPs and 331 metabolites (43 unique fasting and 1-hr metabolites from a 2-hr 75g OGTT conducted during HAPO at ~28 weeks’ gestation and/or metabolites from cord blood collected at delivery), a total of 510 unique SNPs had trimming . Notableal AC C2 . The strUnder the highest penalty term value of λ = 15 , the BNM involving fasting maternal glucose and metabolites estimated edges among a network involving 187 nodes with at least one incident edge and 137 edges. As λ values decreased, the number of nodes in each estimated BNM with at least one incident edge and the total number of edges increased, culminating in a BNM involving 309 nodes and 3070 edges for the lowest value of λ = 1 . Similarly, for the BNM involving 1-hr metabolites and 1-hr maternal glucose, the most stringent penalty of λ = 15 resulted in a BNM with 180 nodes and 132 edges and grew to 306 nodes and 3033 edges at λ = 1. CPS1 connects to glycine. The CPS1-glycine association is well-documented in multiple GWAS studies [Dynamic animation of both the fasting and 1hr BNMs were generated to better visualize areas of increasing edge density over the range of penalty parameters . Nodes w studies ,19,20,21For the fasting and 1-hr BNMs with the least stringent penalty values at λ = 1, all shortest paths from a maternal feature to a newborn outcome (sum of skinfolds (SSF), birthweight, cord C-peptide or cord glucose) were identified. Any path that was a subpath of a larger path was not considered. In total, 3000 unique directed paths from a maternal feature to a newborn outcome were identified in the fasting BNM and 1438 unique paths were identified in the 1-hr BNM. The number of nodes ranged from 3 to 8 in the paths extracted from both the fasting and 1-hr BNMs.For all identified 4438 different pathways, SMM analyses were performed as described. We specifically examined pathways for which the first node was treated as the independent ‘exposure’ variable and the last node, a newborn outcome , was the dependent ‘outcome’ variable, and all nodes in between were treated as potential mediators, i.e., individual mediators for paths with three nodes or serial mediators for paths with four or more nodes. For the 3000 candidate pathways from the fasting BNM, nine demonstrated a statistically significant IDE after false discovery rate (FDR) adjustment and maintained a positive proportion mediated (PM) in the training data set. For the 1438 candidate pathways from the 1-hr BNM, 22 demonstrated a statistically significant indirect effects (IDE) after FDR adjustment and maintained a positive PM in the training data set. When SMM analyses were repeated in the testing dataset, statistically significant serial mediation was validated for five of these pathways , Table 2Integrated modeling of multi-omics data demands careful specification of complex dependencies. For HAPO genetics and metabolomics data, the expected dependencies between observations for mother–offspring pairs merits additional consideration, particularly given the potential for investigating mechanisms underlying fetal programming of related clinical phenotypes in mothers and their newborns using these unique data resources. Recent efforts to identify ‘genetically determined metabotypes’ using classic GWAS approaches in independent individuals identified SNP-metabolite associations with larger effect sizes than typically observed for clinical phenotypes ,22,23,24A key finding upon global review of our integrated analyses is the relative strength of coordinated behavior among classes of maternal and newborn metabolites compared to all other possible dependencies among the variables included in the estimated BNMs. While we may expect high dependency within maternal and offspring metabolites, the relative density of edges connecting maternal and offspring metabolites under the strictest penalty parameters highlights the tight coordination between the metabolisms of a mother and the developing fetus. Of note, network edges between maternal and offspring metabolites and the maternal and offspring clinical phenotypes emerge prior to any edges between the paired clinical phenotypes  and shared maternal/offspring genetics. This observation suggests that environmentally derived or influenced metabolomic contributions may outweigh the genetic underpinnings of fetal programming for size and adiposity at birth.SLC16A9 to cord C-peptide via cord blood acetylcarnitine (C2) and hexadecenoylcarnitine (C16:1), SLC16A9 is a transporter of monocarboxylates that has been associated with C2 levels [Complementing the big picture linking of metabolomics and genetics contributions to maternal/offspring phenotypes is the specific pathway-level interpretation of possible mediating mechanisms. Serial mediation analyses informed by comprehensive BNM estimation indicated biologically plausible mediation mechanisms. In the pathway from 1-hr tyrosine to cord glucose via cord blood tyrosine, phenylalanine and propionyl carnitine (C3), the BNM suggests that higher levels of maternal 1-hr tyrosine indirectly result in lower cord glucose and revealed the plausible intermediate pathways linking these two. It is known that phenylalanine is an essential amino acid and is metabolized to tyrosine when the tyrosine level is low. However, if maternal tyrosine is high and is transported across the placenta leading to sufficient fetal tyrosine, then the conversion of phenylalanine to tyrosine is reduced. C3 is formed from amino acid metabolism but primarily branch chain amino acids (BCAA). Previous studies have shown that the aromatic acids, phenylalanine, tyrosine, and the BCAA, are associated with insulin resistance in adults, including in pregnancy . Thus, i2 levels . Elevate2 levels  and high2 levels . NotablyPenalty parameter selection for sparse estimation of edges in large scale networks is a topic of focused discussion and ongoing bioinformatics research. In contrast to selecting a single penalty parameter that optimizes specific criteria for estimation of one BNM of our integrated data, we opted to view and animate BNMs over a range of penalty parameters. This approach to visualization, along with color distinctions for node types, naturally facilitated the observation of the relative centrality of maternal/offspring metabolite dependencies compared to shared genetics for explaining dependencies between clinical phenotypes that were specifically investigated in HAPO. For other studies involving multi-omics data, exploring network growth over decreasing stringency requirements may illuminate the relative strengths and cooperativity of different types of omics features. There are limitations to our analytic approach. In our BNM analyses, we blocklisted potential edges from offspring SNPs to maternal phenotypes and fasting/1-hr maternal metabolites such that no such edge could be included in the estimated models. However, there is evidence that offspring genotype can have limited influence maternal phenotypes ,32. ImplIn summary, Bayesian network modeling and animated visualization over a range of penalty parameters provided a natural analytic framework for the cogent synthesis of genetics and metabolomics data related to clinical phenotypes for HAPO mothers and their newborns. This approach facilitated both large-scale summaries of the relative contributions of data types, as well as local interrogation of potential serial mediating pathways. These pathways may suggest unique lines for further laboratory and/or population-level investigation, potentially incorporating other edges from the BNM that influence the linear serial mediating pathway of primary interest. Beyond the HAPO study, the approaches described here can be adapted to integrate omics data for other studies that are designed to evaluate complex contributions that may underlie clinical phenotypic associations.The HAPO Study was an observational, population-based study conducted from 2000–2006 at 15 international field centers that recruited over 25,000 participants . EnrolleConventional clinical and targeted metabolites were measured in 3463 maternal fasting and 1-hr serum samples  and in 1http://www.amdis.net/ accessed on 22 June 2021 [2 transformed. QC data were then used to account for technical variability on a feature-specific basis, with batch correction and normalization performed as described using the metabomxtr R package (version 1.26.0) [Non-targeted assays were performed using gas chromatography-mass spectrometry (GC-MS) as described . Brieflyune 2021 ) and annune 2021  with add 1.26.0) ,43. Non-Genotyping and QC for HAPO data have been described for an initial set of 2581 maternal and newborn Afro-Caribbean and 1615 maternal and newborn Mexican American DNA samples , 3152 maternal and newborn Northern European samples , and 2466 maternal and newborn Thai samples  .−4 for the original Afro-Caribbean, Mexican American, Northern European, and Thai groups, <1 × 10−6 for the additional 2680 Northern European group, and <1 × 10−3 for the transethnic group), duplicate discordance, sex differences in heterozygosity, low minor allele frequencies (<0.01) and/or low imputation quality score (<0.75).In addition, a subset of 2680 Northern European maternal and newborn samples were run using the Illumina Human 610 Quad v1 B SNP array at the Broad Institute Center for Genotyping and Analysis (CGA), USA , and a t2 filter of 0.3 to remove unreliably imputed SNPs. A total of 5,237,318 overlapping SNPs were analyzed in a mega-analysis across the twelve imputed datasets. Principle components (PCs) were estimated separately for maternal and offspring data using the package SNPRelate version 1.26.0 in R [Genotype imputation was performed separately on the twelve datasets  on the TOPMed Imputation Server using Minimac4 (version 1.5.7)  and the 6.0 in R .n = 924) were used as a training data set for Bayesian network model development and selection of potential mediation pathways. The remaining one-third (n = 461) were used as a validation data set for confirming statistical significance of purported mediation findings. Sample selection for training and validation data sets was stratified by ancestry group based on genotype data.For analyses reported here, 1385 HAPO mother/offspring pairs with all of the following data types were retained for analysis: (1) maternal genotype, (2) offspring genotype, (3) maternal fasting and 1-hr targeted and non-targeted metabolomics, (4) offspring cord blood targeted and non-targeted metabolomics, (5) maternal fasting and 1-hr glucose measurements and maternal BMI from the HAPO OGTT at ~28 weeks’ gestation along with additional maternal data used for model adjustments, and (6) offspring sum of skinfolds, birthweight, cord glucose, and cord C-peptide measured at delivery. Two-thirds of these samples  analyses were performed separately for all metabolites as outcomes ,26. Meta6.0 in R .10 fasting plasma glucose at OGTT, square root of 1-hr plasma glucose at OGTT , the 43enotypes ). A simpenotypes . Conditiigraph R package [lavaan R package version 0.6-11 [p-values < 0.05) and a positive proportion mediated were evaluated for replication in the testing dataset according to nominal p-value < 0.05.Shortest directed paths in the estimated Bayesian network under the lowest λ value  were identified as possible candidates for mediation analyses using shortest path identification functionality in the  package . In partn 0.6-11 . The sern 0.6-11  was adapn 0.6-11 . SMMs weigraph version 1.2.4.1 [intergraph version 2.0-2 [networkDynamic version 0.10.0 [network version 1.15 [ndtv version 0.13.0 [x-axis on the.html files that contain the network animations index the increasing density of the BNM estimates, i.e., an index of 1 on the x-axis in the animation corresponds to λ = 15, an index of 2 to λ = 14, and so on.We generated interactive visualizations to assist in the interpretation of BNMs over a range of penalty parameters using functions from the  1.2.4.1 , intergron 2.0-2 , networkn 0.10.0 , networkion 1.15 ,56, and n 0.13.0  R packag


Over the last two decades of biological research, our understanding of how genes determine dental development and variation has expanded greatly. Here, we explore how this new knowledge can be applied to the fossil record of cercopithecid monkeys. We compare a traditional paleontological method for assessing dental size variation with measurement approaches derived from quantitative genetics and developmental biology. We find that these new methods for assessing dental variation provide novel insight to the evolution of the cercopithecid monkey dentition, different from the insight provided by traditional size measurements. When we explore the variation of these traits in the cercopithecid fossil record, we find that the variation is outside the range predicted based on extant variation alone. Our 21st century biological approach to paleontology reveals that we have even more to learn from fossils than previously recognized.Advances in genetics and developmental biology are revealing the relationship between genotype and dental phenotype (G:P), providing new approaches for how paleontologists assess dental variation in the fossil record. Our aim was to understand how the method of trait definition influences the ability to reconstruct phylogenetic relationships and evolutionary history in the Cercopithecidae, the Linnaean Family of monkeys currently living in Africa and Asia. We compared the two-dimensional assessment of molar size  to a trait that reflects developmental influences on molar development  and two traits that reflect the genetic architecture of postcanine tooth size variation . All traits were significantly influenced by the additive effects of genes and had similarly high heritability estimates. The proportion of covariate effects was greater for two-dimensional size compared to the G:P-defined traits. IC and MMC both showed evidence of selection, suggesting that they result from the same genetic architecture. When compared to the fossil record, Ancestral State Reconstruction using extant taxa consistently underestimated MMC and PMM values, highlighting the necessity of fossil data for understanding evolutionary patterns in these traits. Given that G:P-defined dental traits may provide insight to biological mechanisms that reach far beyond the dentition, this new approach to fossil morphology has the potential to open an entirely new window onto extinct paleobiologies. Without the fossil record, we would not be able to grasp the full range of variation in those biological mechanisms that have existed throughout evolution. The most essential, core moment in paleontology is when someone notices a fossil as something other than a rock and collects it for scientific study. This event is often just a person walking across the landscape, scanning the ground for evidence of past life. While this simple act has been fundamentally the same for generations of paleontologists, the lead-up to that moment and the science that follows have evolved dramatically. The technological advances that have taken us from landline telephones to smartphones have similarly altered how the science of paleontology is conducted. We can see this in the way scientists discover fossil sites. Where fossiliferous sediments were once identified mostly by happenstance, aerial photography, then satellite imagery, and now remote sensing are common tools for field paleontologists ,2,3. As The laboratory side of the science is also remarkably different from 20th century paleontology. Fossils are now imaged by laser scanners as well as through photography ,7. QuantPaleontologists have also incorporated new knowledge from biology and genomics. As genomic sequencing became increasingly possible for a wide range of organisms, paleontologists began to combine morphological evidence from fossils with genomic data to reconstruct phylogenetic relationships ,32,33.Alongside the genomic revolution, there is another discipline in biology with significant implications for paleontology: elucidating the relationship between genotype and phenotype, often referred to as genotype:phenotype (G:P)-mapping. The insight that comes from G:P-mapping will fundamentally alter how we approach fossil morphologies in the 21st century and, consequently, improve our knowledge of the evolutionary past. To demonstrate this point, we investigated the insight that G:P-mapped dental traits bring to the African fossil record of monkeys (Primates: Cercopithecidae). We first used quantitative genetic analyses to assess the heritability and covariate effects on traditional measurements of tooth size and two types of G:P-mapped traits, one derived from developmental biology and the other from quantitative genetic analyses. We then compared how these traits vary across extant cercopithecids to test Hypothesis 1: G:P-mapped dental traits can provide evidence of phylogenetic history and selection, and therefore, are useful in paleontological investigations. We then focused on the traits defined through our quantitative genetic approach and explored how they vary in the fossil record to test Hypothesis 2: G:P-mapped traits reveal a range of morphological variation that cannot be predicted solely through extant variation.Paleontologists have long relied on the size of the postcanine teeth  to serve as a proxy for body size, to provide essential insight into taxonomy, and to observe patterns of evolution ,35,36,37Over the last couple of decades, technological advances in the biological sciences have enabled scientists to probe the genetic influences on tooth size variation. There are two main avenues for G:P-mapping of dental variation: quantitative genetics and developmental biology. Quantitative genetic analyses approach the G:P-map through phenotypic variation, investigating how anatomical variation is inherited through family lineages. So long as the family structure within a population is known, any taxon can be studied, including large-bodied and long-lived animals such as primates. Because quantitative genetics reveals the genetic contributions to phenotypic variation within a population, this approach is particularly informative for Neogene paleontology, as population-level variation is most applicable to micro-evolutionary questions ,39. In cWhile there is a deep history of quantitative genetic research on the dentition , resultsIn order to make this quantitative genetic evidence translatable to paleontological research, Hlusko and colleagues  developeWe also defined PMM as a ratio that reflects the genetic correlation between the size of the fourth premolar relative to the size of the molar module . PreviouThe mandibular versions of MMC and PMM were first identified for cercopithecid monkeys and then expanded to apes, revealing an episode of selection during the Late Miocene . While wThe influence of developmental mechanisms on two-dimensional molar size variation has also been explored. Kavanagh and colleagues  reportedFor Hypothesis 1, we explore both types of G:P-mapped traits in the maxillary dentitions, the IC , and the MMC and PMM (from quantitative genetics). For Hypothesis 2, we focus on the quantitative genetics-derived traits, complementing the previously published investigation of the mandibular versions of PMM and MMC with the maxillary analyses.Papio hamadryas baboons. The extant, neontological analyses were performed using data from 825 museum skeletal specimens representing 13 genera within Cercopithecidae. Finally, we augmented the data we collected from museum specimens with data culled from the published scientific literature to create a fossil dataset of 1,436 individuals from 17 genera representing the last 20 million years of cercopithecid evolution in Africa.Our analyses rely on dental linear metrics from three different samples described in detail in the following paragraphs. The quantitative genetic analyses were performed on data from 611 individuals within a captive pedigreed population of Sample 1, quantitative genetics: The baboons from which dental data used in our quantitative genetic analyses were obtained are members of a large, six-generation pedigree (n = 2426), developed and maintained at the Southwest National Primate Research Center (SNPRC) at the Texas Biomedical Research Institute (Texas Biomed) in San Antonio, Texas. The pedigree was genetically managed to minimize inbreeding, and ascertainment of animals for this study was random with respect to phenotype. We analyzed linear crown metric data for the maxillary fourth premolar and first, second, and third molars obtained from 611 members of the single, large, six-generation pedigree. The female to male sex ratio was approximately 2:1 and the mean age of the sample was approximately 16 years, with ages ranging from 8 to 32 years. All procedures involving animals were reviewed and approved by Texas Biomed’s Institutional Animal Care and Use Committee. SNPRC facilities and animal use programs at Texas Biomed are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International, comply with all National Institutes of Health and U.S. Department of Agriculture guidelines, and are directed by Doctors of Veterinary Medicine.Sample 2, extant variation: Our comparative sample of extant taxa includes 825 individuals . For thAbbreviations: Premolars are abbreviated as P, molars as M. The letter for the tooth (P or M) is followed by a number indicating tooth position. For example, M2 refers to the second molar. We are primarily focused on a discussion of maxillary molars in this manuscript. We specifically indicate if a measurement or tooth is from the mandibular dental arch in the text rather than through abbreviations.Overview: In order to test Hypothesis 1, we first established that a significant proportion of the phenotypic variation in all of the six traits is attributable to the effects of genes, i.e., that all the traits are heritable. To do this, we estimated the heritability of the traits in the SNPRC baboons. We then assessed the variation of all six traits across a sample of extant cercopithecid monkeys and considered how they vary within a phylogenetic context through a phylogenetic ANOVA. We followed the ANOVA with an analysis to test whether the traits are phylogenetically conserved or show evidence of selection. For the test of Hypothesis 2, we focused on the two traits derived from quantitative genetics: PMM and MMC. We first reconstructed ancestral states (ASR) based on the phylogenetic relationships of the extant genera analyzed for Hypothesis 1. We then compared the ASR trait values derived from the extant taxa to the PMM and MMC values observed in the fossil record.Quantitative genetic analyses: We conducted statistical genetic analyses using a maximum likelihood-based variance decomposition approach implemented in the computer package SOLAR ([www.solar-eclipse-genetics.org). This approach partitions the observed covariance between individuals into genetic and environmental components. The variance components are additive, with the phenotypic variance (h2), the proportion of the phenotypic variance attributable to additive genetic effects, were obtained as: e SOLAR . Significance of the maximum-likelihood estimates for heritability and other parameters was assessed by means of likelihood ratio tests . The maxry value . In bothry value . However p-value . Descriptive statistics: Statistical analyses were completed in the R statistical environment v3.2.2 [moments package in R [ggplot2 .Phylogenetic ANOVA: We conducted a phylogenetic ANOVA to investigate variation across cercopithecid genera using the aov.phylo function in geiger [n geiger . The phyPhylogenetic analyses: For all phylogenetic analyses, we used a consensus molecular chronogram based on a Bayesian phylogenetic analysis of genetic data downloaded from the 10kTrees v.3 database, built using data from six autosomal genes and 11 mitochondrial genes sampled from GenBank [Presbytis rubicunda is not available in the 10kTrees database, and so we added this taxon manually to the phylogeny in R using a branch length split age of 1.3 million years from Presbytis melalophos [ GenBank . Presbytlalophos ,86.Test of phylogenetic signal and selection: We tested the phylogenetic signal of the dental traits with a Blomberg’s K analysis using phylosignal in picante [ picante . Blomber picante . The K v picante ,89. We uAncestral state reconstruction: To investigate how dental traits have evolved in cercopithecids, we generated a series of ancestral state reconstructions (ASR) using contMap in phytools [phytools [phytools , which mphytools , a funct2 estimates, ranging from 0.611 to 0.728, were obtained for five of six two-dimensional areas, two on the left side and three on the right. Both sex and age exerted significant mean effects on the two left side 2-dimensional areas, while only sex influenced the three right side traits. These covariate effects were substantive, accounting for approximately 28% to 51% of the total phenotypic variance in these five 2-dimensional areas. These same analyses returned significant h2 estimates (range: 0.491–0.604) for three of the six G:P-mapped traits: right IC, and right and left PMM, with sex being the lone significant covariate, accounting for approximately 2% to 9% of their total phenotypic variance.The results of the quantitative genetic analyses are presented in The analyses did not return statistically significant heritability estimates for four phenotypes, three on the left side of the arch  and one on the right (MMC). Derivation of these traits was based on data from comparatively small numbers of animals: i.e., only 140 to 221 individuals of the more than 600 pedigreed baboons from which data were obtained for this study.Extant variation descriptive statistics: Univariate statistics for the two-dimensional areas of M1, M2, and M3, and the G:P-mapped traits  are reported in Phylogenetic ANOVA: Results from the phylogenetic ANOVA are presented in p-values indicate that all six traits differ significantly across the genera included in the analyses. The p-values for each genus are also presented. For two-dimensional areas, Nasalis, Colobus, Macaca, Lophocebus, and Erythrocebus are not different from the pooled value of the trait across all the extant genera. Piliocolobus is only statistically different for the M2. Chlorocebus is only statistically different for the M2 and M3 two-dimensional areas. IC and MMC results are identical, demonstrating that Cercopithecus, Mandrillus, Papio, and Theropithecus are statistically significantly different from the pooled values of IC and MMC. PMM differentiates most of the papionins  as well as the colobine Nasalis from the other genera.Phylogenetic signal: Blomberg’s K-values for the six traits are reported in p-values indicate that the trait is evolving neutrally under Brownian motion. IC is marginally significant at the p = 0.05 level, and therefore may indicate that IC variation observed across these extant taxa is the result of selection. MMC is statistically significant at the p = 0.05 level, providing a clear indication that selection has likely been operating on the relative mesiodistal lengths of the molars. Blomberg’s K is a conservative test that is sensitive to sample size [ple size . Additiople size . SamplinAncestral State Reconstruction (ASR): ASR estimates based on the extant genera listed in Comparison to fossil data: In order to compare the ASR trait values to the anatomical variation observed in the fossil record, we compiled data for 17 fossil genera , paleontologists are able to modify their approaches to anatomical variation accordingly. Our aim in this study was to understand how the method of trait definition influences the ability to reconstruct phylogenetic relationships and evolutionary history inCercopithecidae, the Linnaean Family of monkeys currently living in Africa and Asia. We compared one of the most classic traits in primate paleontology, two-dimensional occlusal tooth size , to a trait that reflects developmental influences on molar development  and twon = 191 for the left and 140 for the right) with data available. We are confident that this non-significant result is due to the analysis being underpowered rather than a true biological signal, given that the component dimensions when analyzed individually are highly heritable [We first established that our maxillary trait types are highly heritable , indicating that variation in tooth size, however it is assessed, is significantly influenced by genetic variation. This result was expected, as it builds on many decades of quantitative genetic analyses of dental variation demonstrating that tooth size is one of the most heritable phenotypes . At fireritable ,50,51, aeritable . Howevern = 170) compared to the right (n = 127), only the right value returned a significant heritability estimate for IC. This is likely the result of where those individuals with data fall in the pedigree rather than evidence of a different biological signal. We are currently in the process of expanding the SNPRC dental data set and anticipate revisiting these analyses with a larger sample size.These quantitative genetic analyses provide a good example of how challenging this approach can be, and why this type of research within evolutionary biology is only now becoming more common. Sampling is a significant challenge. For example, in our data set for the SNPRC baboons, composite traits reduce the number of individuals that can be included by a remarkable degree, especially for traits that include measurements of the third molar. We see this data reduction because the SNPRC measurements were collected from dental casts made of living animals. Consequently, the gumline often obscures the back edges of the third molar. Therefore, in a sample of 611 animals within the SNPRC colony, we only have M3 mesiodistal lengths for 140 (right side) and 191 (left side) individuals. Another significant factor in the success of quantitative genetic analyses is the location of the individuals within the pedigree. For example, even though we have more SNPRC baboon individuals available for the analysis of the left IC . Additionally, the IC and MMC have the same pattern of significance across genera in our ANOVA. This molar module pattern is distinct from the PMM, providing additional evidence that PMM is capturing a genetic mechanism distinct from that of the MMC (and IC). Our estimation of Blomberg’s K also reveals similarities between MMC and IC. However, the results presented here suggest that our measurement protocol for MMC may well be a more specific reflection of the underlying genetic mechanism influencing molar proportions in cercopithecids compared to IC, given that we removed the known pleiotropic effects with body size. Further genetic analyses are needed to explore this with more certainty.http://evomorph.org/inhibitory-cascade, accessed on 17 July 2022). With evolutionary biologists expressing this type of sentiment about the utility of fossils, it would not be unreasonable for funding agencies and budding scientists to ask if field paleontology is a thing of the past. Does the future of paleontology need new fossils?There has been a lot of enthusiasm for what G:P-mapped dental traits might offer for oral health  as well In light of this question, our second major aim was to investigate G:P-mapped traits within the fossil record. For this, we focused on the maxillary MMC and PMM and compared computer-generated estimates of ancestral traits to the traits observed on fossils. We want to be up front about there being no clear consensus on direct ancestor-descendant relationships among cercopithecids over the last five million years, as the African cercopithecids from the Plio-Pleistocene are remarkably different from extant monkeys ,99. ConsPapio and Theropithecus as newly derived, when we see that they actually have quite similar PMM values to early papionin genera such as Parapapio, Pliopapio and Soromandrillus. The high MMC values of the Miocene and Pliocene colobines also change how we view the evolutionary relationship of the African and Asian colobines. Knowing that earlier colobines in Africa had higher MMC values than both extant African and Asian colobines suggests that the African and Asian colobines evolved along the same MMC trajectory (reducing the MMC over time). None of these trends are visible when just size alone is considered.Our comparisons of the ASR estimates with the fossil values unequivocally demonstrate that ASR based on extant data is compromised by the phenomenon of “the tyranny of the present”. The lure of the extant comparative data available in museum collections unintentionally limits our expectations for what ancestral morphologies could have been. For example, we find that both MMC and PMM ASR estimates return values lower than what is observed in the fossil record penecontemporaneous with the ancestral nodes . PMM is The next step is to figure out what genetic mechanisms MMC (and IC) and PMM capture. We have a few hints. Previous analyses have shown that mandibular MMC is likely more evolutionarily conserved than PMM within catarrhine primates , across


This study examines the impact of G × E interaction effects on non-alcoholic fatty liver disease (NAFLD) among Mexican Americans in the Rio Grande Valley (RGV) of South Texas. We examined potential G × E interaction using variance components models and likelihood-based statistical inference in the phenotypic expression of NAFLD, including hepatic steatosis and hepatic fibrosis (identified using vibration controlled transient elastography and controlled attenuation parameter measured by the FibroScan Device). We screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for hepatic fibrosis × BDI-II. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis. The Rio Grande Valley (RGV) of South Texas is one of the poorest regions of the United States and experiences significant health disparities. The majority 90%) Mexican American population of the RGV faces disproportionately high rates of obesity (55.5%), diabetes 32.5%), and depression 19%) .5%, and 0% MexicaThe pathogenesis of NAFLD results from the accumulation of triglycerides (TGs) in hepatocytes. The sterol regulatory element-binding protein-1 (STEBP-1), activated by insulin and the carbohydrate response element-binding protein (Ch REBP), glucose and fructose, and the peroxisome proliferator active receptor (PPAR) gamma, controls lipogenesis –12. As hWe can measure liver health by liver biopsy, magnetic resonance imaging, ultrasound, and vibration-controlled transient elastography (VCTE by FibroScan) , 13. AltThe presence and severity of NAFLD are associated with depression , 18–23. This study examines the impact of G × E interaction effects of NAFLD and depression among Mexican Americans in the RGV of South Texas. We are interested in the role of genetics and the environment in our population, specifically, if there is evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis.The University of Texas Rio Grande Valley IRB approved the study protocol. All participants provided informed consent prior to participating in the study. We evaluated 279 Mexican American participants recruited from the community in an ongoing genetic study for the presence of obesity, diabetes, hypertension, hyperlipidemia, and depression. Information gathered included biometric data, an assessment of depression , and VCTE results. Inclusion criteria included age of 18 years or older, residence in the RGV, and having four grandparents who are either Mexican or Mexican American.The BDI-II was used to assess the degree of depressive symptoms present over a 2-week period . The BDI2) and genotype × environment interaction using a variance component approach as implemented in the freely available computer program SOLAR.1 Each liver-related phenotype (CAP and kPa) was regressed against age, sex, age-squared, sex-by-age, and sex-by-age-squared, and then the regression residuals derived for each trait were normalized using an inverse normal transformation  complete pleiotropy  in which the genetic correlation (ρg) is one across environments: ρg = 1. There is G × E evidence if either null hypothesis is rejected  that the pairwise genetic correlation across environments is unity, and that the additive genetic variance is homogeneous. Under the G × E model, we relax these assumptions by expressing both the additive genetic variance and genetic correlations as continuous functions of a specific environment  to capture any potential interaction between the genetic effects  and the specific environment. The null hypothesis is that the expression of the aggregate of all genotypes underlying a phenotype (polygenotype) is independent of the specific environment. Rejection of the null hypothesis implies that the genotype–phenotype map for the trait in question depends on a specific environment or is a function of the specific environment. We begin to study the problem of the genotype–phenotype map potentially being dependent on the environment by modeling the G × E interaction variance. The G × E interaction variance is zero if the following two conditions are simultaneously true: (1) homogeneity of the additive genetic variance across environments: σrejected . Rejectip < 1.0 × 10–5). Because we are interested in the purely environmental component of depression, we computed a prediction of the associated genetic values using Best Linear Unbiased Prediction (BLUP) methods. BLUP accounts for additive genetic and environmental covariances among relatives based on known pedigree structure ], where αg and γg are parameters to be estimated. Taking the natural logarithm of the exponential function, the variance homogeneity null hypothesis holds for a slope-term equal to 0: γg = 0. The genetic correlation was modeled using the exponential decay function of the pairwise differences in BDI scores: ρg = exp [−λ| BDIx − BDIz|] where BDIx and BDIz are the values of the BDI for any two individuals x and z. The null hypothesis that the genetic correlation is equal to 1 is equivalent to λ = 0 because in this event: ρg = exp [−λ| BDIx − BDIz|] = e0 = 1.For the genetic variance function , we modeled the variance using an exponential function of depression, where the exponential function maintains positivity, which is required of a variance  σ2g = exg, γe, and λ; αg and αe are re-parameterized versions of the variances). The LRT statistic for this comparison is distributed as a 50:50 mixture of Chi-squares with 2 and 3 degrees of freedom (df) . It is important to note that the polygenic model is fully nested within the G × E interaction model and that relative to the polygenic model, the G × E interaction model has three additional parameters (γdom (df) , 43, 46.g, γe, and λ) should even be included at all by comparing its maximum likelihood estimate (MLE) to its standard error (SE). A parameter is roughly significant if its MLE is greater than twice its SE based on likelihood theory. Therefore, if a parameter SE was greater than its MLE, we judged that parameter to be statistically unimportant. Further, the additional parameters were formally tested by the tests mentioned above. If any of the three additional parameters were found to have SEs greater than their MLEs and if these were found to be formally insignificant, we then compared a reduced version of the G × E interaction model to the polygenic model, excluding the insignificant parameters.In the second stage, we examine the more specific G × E interaction hypotheses. The full G × E model with all parameters estimated was compared with models when either gamma (γ) or lambda (λ) was constrained to 0 to, respectively, test the hypotheses of additive genetic variance homogeneity and a genetic correlation equal to 1. The distributions of the LRT statistics are, respectively, a Chi-square with 1 df, and a 50:50 mixture of a Chi-square with a point mass at 0 and a Chi-square with 1 df , 46. As t-tests assuming unequal variances.The demographic characteristics  by sex of the cohort are listed in 2 = 0.37, p < 0.01) and steatosis . We formally compared the full G × E interaction model to the polygenic model for both kPa and CAP  were analyzed. As reported in  and CAP .It is important to note that full G × E interaction model has three additional parameters of interest compared to the basal polygenic model, one of which has a null hypothesis on the boundary of its permissible parameter space. For this reason, the formal comparison gives a 50:50 mixture of Chi-squares with 2 and 3 df. To ensure best-model selection, we took under consideration that the two “slope” parameters, which allow for genetic and environmental variance heterogeneity, both had SEs larger than their respective MLEs, whereas the MLE for the genetic correlation decay parameter was larger than its SE.Formal 1 df testing of the genetic and environmental variance heterogeneity parameters showed that they are not significant for both CAP and kPa , 5. On tp < 1.1 × 10–11) and the heritability of hepatic fibrosis was 0.5  (We investigated the impact of G × E interaction on NAFLD variation in Mexican Americans from the RGV. We determined that both hepatic fibrosis and steatosis were moderately heritable. Our heritability findings are consistent with those of earlier studies  . Cohort-× 10–11) . The her× 10–11) , 51.Employing variance component models, likelihood-based statistical inference, and further refinement with the BLUP-computed depression variable, we found that the response of fibrosis to the depression environment is heritable; the G × E interaction variance is significant.p < 0.001)  . Increas< 0.001) , 54. Gro< 0.001) –59. Ther< 0.001) . The pre< 0.001) .The role of IL-17 in depression and NAFLD, as well as comorbid visceral adiposity and atherosclerosis, is also well documented. A strong relationship was found between the IL-17-related chemokine eotaxin and Intimate-Media Thickness . The association found between the amount of visceral fat and circulating levels of eotaxin and IMT could reinforce the hypothesis that IL-17, released by the visceral adipose tissue, induces eotaxin secretion via the smooth muscle cells present in the atheromatous vessels .It appears that an inflammatory process mediates many chronic illnesses through upregulation of exclusively pro-inflammatory gene expression. This is consistent with our finding that the genetic correlation function for fibrosis decays away from 1  with increasing differences in the depression environment between any two given individuals, which indicates that individuals at different ends of the depression “spectrum” may be expressing different sets of genes. Concerning inflammation, this may demonstrate a progression from a more neutral set of genes to a relatively pro-inflammatory set of genes.n = 279). Future research will focus on larger sample sizes and may identify other potential interactions. Our focus is on Mexican Americans, and future research will determine if the finding of genotype by environment interaction effects between NAFLD and depression is replicated in other populations.The findings in this report are based on a relatively small  using the BDI-II and identified significant G × E interactions for hepatic fibrosis and depression. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis. Future directions will focus on identifying the nature of the interactions and the specific genes involved.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by The University of Texas Rio Grande Valley Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.EM, SL, JB, and SW-B contributed to the conception and design of the study. SL organized the database. VD performed the statistical analysis. KS helped write the first draft. All authors contributed to the manuscript, and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.