A powerful approach to identify replicable variants in genome-wide association studies.

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Assessment of the quality of life in patients with LGMD. The case of transportinopathy.

The Quality of Life (QOL) is influenced by several disease-related factors, support, resources, expectations, and aspirations, within the disease-related concepts. The Individualized Neuromuscular Quality of Life (INQoL) is a validated muscle disease-specific measure of the QoL developed from the experiences of patients with muscle disease and can be used for people or large cohorts. This review of QoL in transportinopathy cases reports adjustments in an autosomal dominant (AD) LGMD, and a comparison is made with autosomal recessive (AR) LGMD evaluated by INQoL. The locus for this form of LGMD with AD inheritance was found on chromosome 7, and then identification of the gene and its encoded protein (transportin-3) was obtained in 2013. A large three-generation family with several branches in Spain and Italy was previously reported and described in detail. Some patients had an early onset weakness, but others had an adult onset of the disease, as late as 58 years. The severity of the appearance of the phenotype is correlated with QoL and progresses with age. Assessing the impact on their QoL is particularly relevant to know whether the treatment is reducing their suffering.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Circulating immune cell phenotyping is potentially relevant for diabetic retinopathy risk assessment.

BACKGROUND: Inflammation is believed to play a central role in the development of diabetes mellitus and is a common feature of type 2 diabetes mellitus (T2DM). However, the association with diabetic retinopathy (DR) remains a topic of debate. METHODS: This study employed two-sample bidirectional Mendelian randomization (MR) analyses to establish causal associations between immune cell characteristics and DR. Using publicly available GWAS genetic data, we investigated the causal relationship between 731 immune cell characteristics and the risk of DR. A total of four types of immune features, including relative cell (RC), absolute cell (AC), median fluorescence intensities (MFI), and morphological parameters (MP), were included. Sensitivity analysis was conducted to assess the robustness, heterogeneity, and potential horizontal pleiotropy of the results. RESULTS: Thirty-five immune cell phenotypes were correlated with the risk of developing DR among four immune traits (MFI, RC, AC, and MP), and DR resulted in altered expression of twenty-six immune cells. CONCLUSION: We have demonstrated a strong correlation between immune cell traits and DR using a genetic approach. This finding offers valuable insights for early DR prevention and future clinical research and treatment.

Multi-trait selection for nutritional and physiological quality of cacao genotypes in irrigated and non-irrigated environments.

Water is a scarce, strategic resource and the most important input for economic development, especially in agricultural countries such as Brazil. Cocoa production is directly related to water availability, and, as climate changes, selecting drought-tolerant genotypes is vital to keep cacao crops sustainable. Here, we evaluated cacao genotypes under irrigated and water-stressed conditions and selected drought-tolerant ones based on nutritional and physiological traits. Thirty-nine genotypes were monitored for three years for agronomic traits and higher fruit yield. After this evaluation, the 18 most promising genotypes were evaluated in a randomized block design, under a 2 (with and without irrigation)  ×  18 (genotypes) factorial arrangement, with three replicates and five plants per plot. We evaluated seven physiological and 11 nutritional traits, selecting genotypes based on the Genotype-by-Trait Biplot approach. Significant effects (p < 0.05) were observed for the nutritional traits N, P, Mg, S, Zn, Cu, Mn and for the physiological traits CO2 assimilation rate (A), stomatal conductance (gs), transpiration (E), intercellular and atmospheric CO2 concentrations (Ci/Ca), intrinsic water use efficiency (A/gs), instantaneous water use efficiency (A/E), and instantaneous carboxylation efficiency (A/Ci), as determined by analysis of variance. The genotype  ×  irrigation treatment interaction was significant (p < 0.05) for the traits A, gs, and E. Genotypes CP 41, CP 43, and CCN 51 exhibited superior performance for both nutritional and physiological traits (A, gs, and E). In the irrigated environment, CP 41 showed superiority in traits such as P, A/E, A/gs, Mn, S, and Zn. Conversely, under non-irrigated conditions, CP 43 exhibited better performance in nutritional properties, specifically Mn, Mg, and Zn. Notably, in both irrigated and non-irrigated environments, CCN 51 excelled in key physiological traits, including A/Ci, A/E, and A/gs. This robust performance across diverse conditions suggests that these three genotypes possess physiological mechanisms to endure water-stressed conditions. Our research can generate valuable insights into these genotypes informing suitable choices for cocoa cultivation, especially in the context of global climate change.

Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes.

The objective of the present study was to identify genomic regions influencing economic traits in Murrah buffaloes using weighted single step Genome Wide Association Analysis (WssGWAS). Data on 2000 animals, out of which 120 were genotyped using a double digest Restriction site Associated DNA (ddRAD) sequencing approach. The phenotypic data were collected from NDRI, India, on growth traits, viz., body weight at 6M (month), 12M, 18M and 24M, production traits like 305D (day) milk yield, lactation length (LL) and dry period (DP) and reproduction traits like age at first calving (AFC), calving interval (CI) and first service period (FSP). The biallelic genotypic data consisted of 49353 markers post-quality check. The heritability estimates were moderate to high, low to moderate, low for growth, production, reproduction traits, respectively. Important genomic regions explaining more than 0.5% of the total additive genetic variance explained by 30 adjacent SNPs were selected for further analysis of candidate genes. In this study, 105 genomic regions were associated with growth, 35 genomic regions with production and 42 window regions with reproduction traits. Different candidate genes were identified in these genomic regions, of which important are OSBPL8, NAP1L1 for growth, CNTNAP2 for production and ILDR2, TADA1 and POGK for reproduction traits.

Genetic similarity between relatives provides evidence on the presence and history of assortative mating.

Assortative mating - the non-random mating of individuals with similar traits - is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities.

Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations.

SUMMARY: Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. AVAILABILITY AND IMPLEMENTATION: Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.

A new proposal for phenotypic classification and outcome assessment of dermatomyositis based on clinical manifestations and serological testing.

BACKGROUND: Dermatomyositis (DM) is an infrequent disease subgroup of idiopathic inflammatory myopathies characterized by distinct skin lesions. However, high heterogeneity makes clinical diagnosis and treatment of DM very challenging. OBJECTIVES: Unsupervised classification in DM patients and analysis of key factors related to clinical outcomes. METHODS: This retrospective study was conducted between 2017 and 2022 at the Department of Rheumatology, Xiangya Hospital, Central South University. 162 DM patients were enrolled for unsupervised hierarchical cluster analysis. In addition, we divided the clinical outcomes of DM patients into four subgroups: withdrawal, stabilization, aggravation, and death, and compared the clinical profiles amongst the subgroups. RESULTS: Out of 162 DM patients, three clusters were defined. Cluster 1 (n = 40) was mainly grouped by patients with prominent muscular involvement and mild Interstitial Lung Disease (ILD). Cluster 2 (n = 72) grouped patients with skin rash, anti-Melanoma Differentiation Associated protein 5 positive (anti-MDA5+), and Rapid Progressive Interstitial Lung Disease (RP-ILD). Cluster 3 (n = 50) grouped patients with the mildest symptoms. The proportion of death increased across the three clusters (cluster 3 < cluster 1 < cluster 2). STUDY LIMITATIONS: The number of cases was limited for the subsequent construction and validation of predictive models. We did not review all skin symptoms or pathological changes in detail. CONCLUSIONS: We reclassified DM into three clusters with different risks for poor outcome based on diverse clinical profiles. Clinical serological testing and cluster analysis are necessary to help clinicians evaluate patients during follow-up and conduct phenotype-based personalized care in DM.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.

Correlative variation of economically valuable traits in South Kazakh Merino.

Fine-fleeced sheep are distinguished by numerous economically valuable traits that constitute the foundation for productive distinctions among breeds, populations, lines, and individuals. Many of these traits have already been mentioned or thoroughly examined during studies on the correlative variability of productivity indicators, blood parameters, characteristics of pulmonary gas exchange, histological structures of the skin, and features of the experimental sheep's coat. The objective of our research was to investigate the correlative variability of key economically valuable traits that characterize the overall functional state of sheep organisms under varying environmental conditions. The study was conducted at the "Sharbulak" breeding farms and the "Samat" peasant farms in the Kazygurt district of the Turkestan region. Our findings reveal that one-year-old rams surpass ewes in terms of live weight by 32-37% and in terms of unwashed wool shearing by 21-23%. Two-year-old rams outperform ewes in live weight by 2.15-2.17 times and in unwashed wool shearing by 2.38-2.44 times. The highest phenotypic variability in relative terms (as indicated by the coefficient of variation) is observed in the shearing of pure wool, with an average coefficient of variation of 18.1% across all sex and age groups of sheep. This is followed by the wool coefficient (17.4%) and the yield of pure fiber (12.5%). For each group of animals, the most substantial phenotypic correlation coefficients were observed between the live weight of sheep and the shearing of unwashed wool. On average, across all groups of sheep at the "Sharbulak" breeding farm, this phenotypic correlation reaches +0.411 ± 0.077. Correspondingly, for the sheep herd at the "Samat" peasant farm, it is +0.326 ± 0.075. The second-highest phenotypic correlation pertains to the cut of unwashed wool and the length of wool (with correlation coefficients of +0.156 ± 0.058 and +0.145 ± 0.057, respectively, for the herds). The third-highest correlation involves live weight and wool length (+0.131 ± 0.085 and +0.105 ± 0.078, respectively). No statistically significant differences were identified in the average correlation coefficients between the live weight of sheep, the shearing of unwashed wool, and the length of the staple among the flocks of sheep at the "Sharbulak" breeding farm and the "Samat" peasant farm.

CKS1B as a potential target for prognostic assessment and intervention in pancreatic cancer and its role in abnormal proliferation and cellular phenotype through mediation of cell cycle signaling pathways.

OBJECTIVES: To investigate the role of cell cycle protein-dependent kinase regulatory subunit 1B (CKS1B) in driving the aggressive and rapid proliferation observed in pancreatic cancer. METHODS: A comprehensive analysis was carried out using raw mRNA information and data from 2 databases: the cancer genome atlas and gene expression omnibus. The differential expression of CKS1B at the mRNA and tissue levels in cancer and adjacent paracancerous tissues were assessed. Additionally, the relationship of CKS1B expression and overall survival (OS) rate was investigated using Kaplan-Meier survival curves. Potential molecular mechanisms by which CKS1B may influence the biological characteristics of pancreatic cancer were explored using resources available within the encyclopedia of RNA interactomes database. RESULTS: The CKS1B exhibited significant differential expression at the mRNA as well as protein levels. A correlation with statistical significance between CKS1B expression and N stage, age, and alcohol consumption was observed. Notably, high CKS1B expression was determined as a predictive factor for worse OS. Furthermore, the analysis revealed a potential synergistic role between CKS1B and the molecule PKMYT1, which could impact the ATR-Chk1-Cdc25 signaling pathway and disrupt the G2/M checkpoint within the cell cycle, ultimately promoting abnormal tumor proliferation. CONCLUSION: The CKS1B may serve as a novel potential prognostic factor in pancreatic cancer and is involved in the abnormal proliferation biology phenotype by mediating cell cycle signaling pathways.

Economic values of traits in the breeding goals defined by different dairy goat breeders' associations in Kenya.

The current study aimed at development of breeding objectives and estimation of the economic values for traits of economic importance to various dairy goat breeders associations. This was achieved through the development of a bio-economic model to derive economic values for the selected traits. The input and output parameters used represented the average performance of Saanen, Toggenburg and Alpine breeds. The gross margin was estimated using the R software. Economic values for milk yield (MY, kg), pre-weaning survival rate (PrSr, %), post-weaning survival rate, (PoSR, %), doe survival rate, (DoSR, %), and kidding rate, (Kr, %) were estimated based on fixed flock size and fixed feed resources scenarios. The findings indicate varied gross margin across the breeds; Saanen (US$ 116.17), Toggenburg (US$ 68.21) and Alpine (US$ -1.11). In both scenarios, the economic values in selected traits were positive for the three breeds except for milk yield in Alpine, which was negative in both fixed flock and fixed feed resources. The positive economic values of selected traits indicated a unit increase in genetic merit of these traits. The developed bio-economic model was able to estimate the gross margin of Saanen, Toggenburg and Alpine breeds represented by Nyanza Dairy Goat Breeders Association (NDGA), Meru Dairy Goat Breeders Association (MDGA) and Dairy Goat Association of Kenya (DGAK), respectively, reared under semi-intensive production system. The study offers an opportunity to evaluate genetic and economic merit of alternative strategies for dairy goats reared in semi-intensive production systems managed by various dairy goat breeders associations.

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil.

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90 days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

Dynamic and scalable assessment of the senescence-associated secretory phenotype (SASP).

Dual-faced cellular senescence is responsible for beneficial biological processes and for age-related pathologies. Senescent cells under stable proliferation arrest develop numerous senescence-associated phenotypes such as the potent pro-inflammatory secretome called the senescence-associated secretory phenotype (SASP). The SASP shapes the senescent microenvironment and influences the biology of adjacent cells, including the modulation of proliferation and migration/invasion, reinforcement/induction of peripheral senescence, and immune cell activity or recruitment. The SASP is a dynamic process with multiple waves of secreted factors described to interlace over a period of many days. Whether the senescence phenotype reaches a mature stable state remains controversial. Overall, the complexity of the context-dependent and timely SASP compositions and its varied microenvironmental impact demonstrate the importance of properly assessing SASP over time. In this chapter, we focus on scalable and dynamic experimental procedures to prepare SASP conditioned medium over time from cells receiving senescence-inducing stimuli. This SASP-containing conditioned medium can be used to assess the composition of the SASP, study SASP-related signaling pathways or evaluate the paracrine microenvironmental impact of senescent cells.

A method of calculating weighted values of objective traits for BLUP of breeding values to achieve relative desired changes.

A method of calculating weighted values for objective traits from the phenotypic records of all animals in a population was devised as an alternative to the conventional method of calculating weighted values from a family selection index. The genetic improvement of this method was verified by Monte Carlo computer simulation. A base population consisting of 10 males and 50 females, and five separate generations, other than the base population that had been randomly selected, was bred for two traits with different heritabilities. The breeding values of animals in generation five were estimated using the bivariate BLUP method. The three different weighted values obtained from this method and two conventional methods for estimated breeding values of the objective traits were used to estimate aggregate breeding values for selection. The results showed that selection using weighted values calculated from all animals in a population resulted in a greater response to selection, especially when the genetic correlation between the two traits was positive, than selection using other conventional methods. The use of the method devised in this study was expected to result in a greater genetic improvement than the conventional family selection index method for pig breeding programs applied in closed herds in Japan.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.

BACKGROUND: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. RESULTS: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. CONCLUSIONS: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Modelling evolutionary tempo and mode using formal morphological spaces and Markov chain principles.

Evolutionary tempo and mode summarize ancient and controversial subjects of theoretical biology such as gradualism, convergence, contingence, trends, and entrenchment. We employed an integrative methodological approach to explore the evolutionary tempo and mode of Lepidosaurian phalangeal formulae (PFs). This approach involves quantifying the frequencies of morphological changes along an evolutionary trajectory. The five meristic characters encoded by PFs are particularly valuable in revealing evolutionary patterns, owing to their discrete nature and extensive documentation in the literature. Based on a pre-existing dataset of PFs from 649 taxa (35 Lepidosauria families, including fossils), from which there exists a unique repertoire of 53 formulations, our approach simultaneously considers phenetic and phylogenetic data. This culminates in a diagram accounting for the phylogenetic dynamic of evolution traversing across different regions of morphospace. The method involves enumerating phenotypical options, reconstructing phenotypes across the phylogeny, projecting phenotypes onto a morphospace, and constructing a flow network from the frequency of evolutionary transitions between unique phenotypic conditions. This approach links Markovian chains and evolutionary trajectories to formally define parameters that describe the underlying transitions of morphological change. Among other results, we found that (a) PF evolution exhibits a clear trend towards reduction in the phalangeal count and that (b) evolutionary change tends to occur significantly between morphologically similar PFs. Notwithstanding, although minor but not trivial, transitions between distant formulas -jumps- occur. Our results support a pluralistic view including stasis, gradualism, and saltationism discriminating their prevalence in a target character evolution.

Assessment of seed- and seedling-related traits in Santalum album (Indian sandalwood) reveals high adaptive potential.

Seed and seedling traits govern plant fitness and persistence and are influenced by the interaction between the plant and its environment. Changing climatic and edaphic conditions will drastically affect early fitnessrelated traits and can alter the demography and species distribution range. It is widely documented that trait variation among populations may increase resilience of tree communities and reduce the risk of extinction under future climates. In the present study, variation in seed and seedling traits were documented from seven populations of Santalum album representing the natural distribution range of the species in the Indian subcontinent. Significant intra-specific variation was documented in seed and seedling traits, indicating high adaptive potential of the species. Further, the measured traits were correlated with climatic variables. No significant correlation was predicted for seed-related traits, while seedling-related traits like shoot and root weight, photochemical reflectance index, relative water content, and root-shoot ratio correlated with different climatic parameters. Variance partitioning revealed predominant combined effect of environment and genotype on seed traits except seed weight, which was governed by genotypic effect. The dominance of genotypic effect was documented for all seed leachate parameters, while seedling-related traits were predominantly affected by the environment. Conservation of sandalwood genetic resources will benefit from the insights gained from the variability recorded in these fitness-related traits, which are likely to affect the adaptive potential of the species.