Biology subjects

Momen, M.

Publications and source records attributed to Momen, M..

11 recordsLinked to original sources

bioRxiv2025

Assessment of Meridic Gel Larval Diet and Meridic Adult Diet for Mass Rearing of Bactrocera dorsalis (Diptera: Tephritidae)

The oriental fruit fly, Bactrocera dorsalis (Diptera: Tephritidae), is a widespread pest in Bangladesh. Sterile Insect Technique (SIT) offers a solution for effectively suppressing this fruit fly species. However, SIT involves mass rearing of fruit fly species in a laboratory where a standardized artificial rearing diet is crucial for ensuring uniform growth, development, and reproduction. In this study, we assessed efficacy of a new formulated gelbased meridic larval diet as well as protein and carbohydrate rich adult diets for the rearing of B. dorsalis in a laboratory condition. Proximate analysis was conducted for our formulated rearing diets to determine the content of moisture, protein, fat, carbohydrate, and ash. Several key biological parameters, including egg hatching rate, pupation rate, pupal weight, adult emergence, adult growth, sex ratio, and flight capacity, were assessed. Statistical analysis using Tukey box plots revealed a significant improvement in adult body parameters and an increase in longevity for our formulated diets. In addition, our study presents survival analysis using non-parametric Kaplan-Meier estimator and Weibull parametric model. This study shows that integrating our formulated diets into the mass rearing process of B. dorsalis in a laboratory could significantly improve the production of larger and healthier flies on a large scale.

zoology↗

bioRxiv2024

Population dynamics of fruit flies (Diptera: Tephritidae) in a semirural area under subtropical monsoon climate of Bangladesh

Fruit flies belonging to Tephritidae family are highly destructive agricultural pests, posing a significant threat to various fruits and vegetables grown in Bangladesh. A comprehensive year-round survey was conducted at Atomic Energy Research Establishment (AERE) campus located in the central region of Bangladesh. Three types of male lures (methyl eugenol, cue-lure and zingerone) were used to detect and assess the diversity of pest fruit fly species. A total of seventeen species of Tephritidae fruit flies were detected in this survey. The Bactrocera carambolae fruit fly has been discovered for the first time in our survey area, indicating spread of its range towards the north-west region from its previous detection sites (Chattogram and Sylhet Divisions) in Bangladesh. Among the detected pest species, we identified six abundant species: Bactrocera dorsalis, Zeugodacus cucurbitae, Zeugodacus tau, Bactrocera rubigina, Bactrocera zonata, and Dacus longicornis. The most abundant species was the polyphagous fruit pest B. dorsalis, comprising 76.83% of all captured flies. The species Z. cucurbitae was the second most abundant, representing 13.82% of the total trapped flies. The fitted curve to survey data using Gaussian mixture model revealed the existence of overlapped subgroups in the population of B. dorsalis and Z. cucurbitae. In addition, our statistical analysis of the six abundant Tephritidae fruit fly species revealed correlation of population dynamics with several factors including temperature, rainfall, humidity, photoperiod, and fruiting time of host plant species in the selected area.

ecology↗

bioRxiv2024

Effect of different lining paper materials and infusions on oviposition preference of Aedes aegypti (Diptera: Culicidae) gravid mosquitoes

Aedes aegypti (Linnaeus) mosquito is a vector, responsible for increasing global public health concerns due to its rapid geographical spread and increasing vectorial capacity. Understanding the breeding behavior of Ae. aegypti is crucial to control this vector and thereby limiting the spread of diseases. To study this mosquito breeding in the laboratories and ovitraps, different types of lining-papers, surrounding inner surface of a container along with different types of added infusions, are used. Lining-papers serve as surfaces for oviposition and infusions provide the essential environment for nurturing. The types of oviposition surfaces and infusions used in laboratory rearing facilities or ovitraps influence oviposition preference, changing the breeding behavior of Ae. aegypti gravid mosquitoes. This study presents a comparative study on oviposition preferences for six different types of lining-papers and six different types of infusions in a bioassay cage. ANOVA analysis shows a significant effect on oviposition preference of different types of lining-papers that served as oviposition surfaces. The highest oviposition activity was observed for the agri seed germination paper-75 lining-paper when normal tap water infusion was used. Likewise, statistical analysis shows that when a plain printing offset paper 80 GSM lining-paper was used, a highly statistically significant effect on oviposition preference is observed for different types of infusions used in the oviposition cups.

animal behavior and cognition↗

bioRxiv2020

Modeling multiple phenotypes in wheat using data-driven genomic explanatory factor analysis and Bayesian network learning

Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum Aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

genetics↗

bioRxiv2020

Genome-wide methylation prediction of biological age using reproducing kernel Hilbert spaces and Bayesian ridge regressions

The use of DNA methylation signatures to predict chronological age and the aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers have been found to be significantly associated with age, most age-prediction methods use a small number of markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4,409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Non-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root-mean-square error (RMSE) of 3.54 years in non-adjusted data, 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with significant hypermethylation in CpG islands and hypomethylation in other CpG types including CpG shore, shelf, and open sea (p < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age compared to males.

genomics↗

bioRxiv2020

Regional genomic heritability mapping for agronomic traits in sugarcane

BackgroundThe identification of genomic regions involved in agronomic traits is the primary concern for sugarcane breeders. Genome-wide association studies (GWAS) leverage the sequence variations to bridge phenotypes and genotypes. However, their effectiveness is limited in species with high ploidy and large genomes, such as sugarcane. As an alternative, a regional heritability mapping (RHM) method can be used to capture genetic signals that may be missed by GWAS by combining genetic variance from neighboring regions. We used RHM to screen the sugarcane genome aiming to identify regions with higher heritability associated with agronomic traits. We considered percentage of fiber in sugarcane bagasse (FB), apparent percentage of sugarcane sucrose (PC), tonnes of pol per hectare (TPH), and tonnes of stalks per hectare (TSH). MethodsSequence-capture data of 508 sugarcane (Saccharum spp.) clones from a breeding population under selection were processed for variant calling analysis using the sugarcane genome cultivar R570 as a reference. A set of 375,195 single nucleotide polymorphisms were selected after quality control. RHM was conducted by splitting the sugarcane genome into windows of 2 Mb length. ResultsWe selected the windows explaining > 20% of the total genomic heritability for TPH (64 windows - 5,654 genes) and TSH (72 windows - 6,050 genes), and > 15% for PC (16 windows - 1,517 genes) and FB (17 windows - 1,615 genes). The top five windows that explained the highest genomic heritability ranged from 20.8 to 24.6% for FB (629 genes), 18.0 to 22.0% for PC (452 genes), 53.8 to 66.0% for TPH (705 genes), and 59.5 to 67.4% for TSH (413 genes). The functional annotation of genes included in those top five windows revealed a set of genes that encode enzymes that integrate carbon metabolism, starch and sucrose metabolism, and phenylpropanoid biosynthesis pathways. ConclusionsThe selection of windows that explained the large proportions of genomic heritability allowed us to identify genomic regions containing a set of genes that are related to the agronomic traits in sugarcane. These windows spanned a region of 58.38Mb, which corresponds to 14.28% of the reference assembly in the sugarcane genome. We contend that RHM can be used as an alternative method for sugarcane breeders to reduce the complexity of the sugarcane genome.

genomics↗

bioRxiv2019

Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models

Environmental association analyses (EAA) seek to identify genetic variants associated with local adaptation by regressing local environmental conditions at collection sites on genome-wide polymorphisms. The rationale is that environmental conditions impose selective pressure on trait(s), and these traits are regulated in part by variation at a genomic level. Here, we present an alternative multivariate genomic approach that can be utilized when both phenotypic and environmental data are available for the population. This framework utilizes Bayesian networks (BN) to elucidate interdependancies between local environmental conditions and empirical phenotypes, and jointly estimates the direct and indirect genetic covariances between empirical phenotypes and environmental conditions using a mixed-effects structural equation model (SEM). Direct genomic covariance between empirical phenotypes and environmental conditions may provide insight into whether QTL that affect adaptation to an environmental gradient also affects the observed phenotype. To demonstrate the utility of this approach, we leveraged two existing datasets consisting of 55 climate variables for 1,130 Arabidopsis accessions and empirical phenotypes for fitness and phenology collected on 515 accessions in two common garden locations in Europe. BN showed that plasticity for fitness and phenology was highly dependant on local environmental conditions. Moreover, genomic SEM revealed relatively high positive genomic correlation between plasticity in fitness and environmental variables that describe the favorability of the local environment for plant growth, indicating the presence of common QTL or independent QTL that are tightly linked. We believe the frameworks presented in this manuscript can provide new insights into the genetic basis of local adaptation.

genomics↗

bioRxiv2019

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Moderate to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.87). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

genetics↗

bioRxiv2019

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, the high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.

genetics↗

bioRxiv2019

Harnessing phenotypic networks and structural equation models to improve genome-wide association analysis

Plant breeders and breeders alike seek to develop cultivars with maximal agronomic value. The merit of breeding material is often assessed using many, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships between traits. With the proliferation of multi-trait genome-wide association studies (MTM-GWAS), we can infer putative genetic signals at the multivariate scale. However, a standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating phenotypic network structures into GWAS using structural equation models (SEM-GWAS). In this network GWAS model, one or more phenotypes appear in the equations for other phenotypes as explanatory variables. A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects into direct and indirect effects. In this paper, we illustrate the utility of SEM-GWAS using biomass, root biomass, water use, and water use efficiency in rice. We found that water use efficiency is directly impacted by biomass and water use and indirectly by biomass and root biomass. In addition, SEM-GWAS partitioned significant SNP effects influencing water use efficiency into direct and indirect effects as a function of biomass, root biomass, and water use efficiency, providing further biological insights. These results suggest that the use of SEM may enhance our understanding of complex relationships between GWAS traits.

genetics↗

bioRxiv2018

Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models

BackgroundPhenotypic networks describing putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effects in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes.\n\nMethodsWe applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among body weight (BW), breast meat (BM), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS).\n\nResultsThree different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM[->]BW, and negative values were obtained for BM[->]HHP and BW[->]HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEMGWAS.\n\nConclusionsAlthough MTM-GWAS and SEM-GWAS use the same probabilistic models, we provide evidence that SEM-GWAS captures complex relationships and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.

genetics↗