An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics.The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations.To benchmark performance,we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios,including steadystate and time-dependent gene expression,and the effects on population heterogeneity of cell growth,division,and DNA replication.This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression.We also use the algorithm to model gene expression dynamics within"bet-hedging"cell populations during their adaption to environmental stress.These simulations indicate that the algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics,relevant physiological details and phenotypic variability.

Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs

Background:Short tandem repeats(STRs)were recently found to have significant impacts on gene expression and diseases in humans,but their roles on gene expression and complex traits in pigs remain unexplored.This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds.Results:We identified 5203 and 5868 significantly expression STRs(eSTRs,FDR<1%)in the F6 and F7 populations,respectively,most of which could be reciprocally validated(π1=0.92).The eSTRs explained 27.5%of the cisheritability of gene expression traits on average.We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs,respectively,which were significantly enriched in intron,ATAC peak,compartment A and H3K4me3 regions.We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs,which colocalized with epigenetic markers such as H3K27ac and ATAC peaks.These included eSTR of the CLPB,PGLS,PSMD6 and DHDH genes,which are linked with genome-wide association study(GWAS)SNPs for blood-related traits,leg conformation,growth-related traits,and meat quality traits,respectively.Conclusions:This study provides insights into the effects of STRs on gene expression traits.The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

Variable selection for skew-normal mixture of joint location and scale models

Although there are many papers on variable selection methods based on mean model in the nite mixture of regression models,little work has been done on how to select signi cant explanatory variables in the modeling of the variance parameter.In this paper,we propose and study a novel class of models:a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population.The problem of variable selection for the proposed models is considered.In particular,a modi ed Expectation-Maximization(EM)algorithm for estimating the model parameters is developed.The consistency and the oracle property of the penalized estimators is established.Simulation studies are conducted to investigate the nite sample performance of the proposed methodolo-gies.An example is illustrated by the proposed methodologies.

MLE with datasets from populations having shared parameters

We consider maximum likelihood estimation with two or more datasets sampled from differ-ent populations with shared parameters.Although more datasets with shared parameters can increase statistical accuracy,this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference.Asymptotic distributions of maximum likelihood estimators are derived under either regulan cases where regularityconditions are satis-fled or some non-regular situations.A bootstrap variance estimator for assessing performance of estimators and/or making large sample inferenceis also introduced and evaluated ina simulation study.

Identification of novel loci influencing refractive error in East Asian populations using an extreme phenotype design

The global "myopia boom" has raised significant international concerns. Despite a higher myopia prevalence in Asia, previous large-scale genome-wide association studies(GWASs) were mostly based on European descendants. Here, we report a GWAS of spherical equivalent(SE) in 1852 Chinese Han individuals with extreme SE from Guangzhou(631 <-6.00 D and 574 > 0.00 D) and Wenzhou(593 <-6.00 D and54 >-1.75 D), followed by a replication study in two independent cohorts with totaling 3538 East Asian individuals. The discovery GWAS and meta-analysis identify three novel loci, which show genome-wide significant associations with SE, including 1 q25.2 FAM163 A, 10 p11.22 NRP1/PRAD3, and 10 p11.21 ANKRD30 A/MTRNR2 L7, together explaining 3.34% of SE variance. 10 p11.21 is successfully replicated.The allele frequencies of all three loci show significant differences between major continental groups(P < 0.001). The SE reducing(more myopic) allele of rs10913877(1 q25.2 FAM163 A) demonstrates the highest frequency in East Asians and much lower frequencies in Europeans and Africans(EAS = 0.60,EUR = 0.20, and AFR = 0.18). The gene-based analysis additionally identifies three novel genes associated with SE, including EI24, LHX5, and ARPP19. These results provide new insights into myopia pathogenesis and indicate the role of genetic heterogeneity in myopia epidemiology among different ethnicities.

RESPONSE OF WHEAT COMPOSITE CROSS POPULATIONS TO DISEASE AND CLIMATE VARIATION OVER 13 GENERATIONS

Since the F_(5)(2005),three winter wheat composite cross populations(CCPs)based on germplasm specifically suitable for low-input conditions were subjected to natural selection under organic and conventional management.In the F_(6),each CCP was divided into two parallel populations(12 CCPs in total)and maintained continuously until 2018.Commonly used modern cultivars with different disease susceptibilities were grown alongside to assess the agronomic performance of the CCPs.The organically managed CCPs were comparable in yield and foliar disease resistance to two continuously used reference cultivars,Achat and Capo.In contrast,under conventional management the cv.Capo outyielded the CCPs(Achat was not tested),highlighting the importance of parental cultivar choice for specific management systems.The CCPs were found to be moderately resistant to brown rust and even to the newly emerged stripe rust races prevalent in Europe since 2011.Differences between the CCPs were mainly due to parental genetic background and were significant in the first five generations,but were no longer so in the last five generations.In addition,these differences tended to vary depending on the experimental year and the environmental stresses present.In conclusion,the CCPs despite being derived from older cultivars are able to compete with more recently released reference cultivars under organic farming practices and represent a dynamic germplasm resource.

Variable selection in finite mixture of median regression models using skew-normal distribution

A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.Moreover,data that arise from a heterogeneous population can be efficiently analysed by a finite mixture of regression models.These observations motivate us to propose a novel finite mixture of median regression model based on a mixture of the skew-normal distributions to explore asymmetrical data from several subpopulations.With the appropriate choice of the tuning parameters,we establish the theoretical properties of the proposed procedure,including consistency for variable selection method and the oracle property in estimation.A productive nonparametric clustering method is applied to select the number of components,and an efficient EM algorithm for numerical computations is developed.Simulation studies and a real data set are used to illustrate the performance of the proposed methodologies.

Energy modeling and data structure framework for Sustainable Human-Building Ecosystems （SHBE）- a review

This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network （SHBE-RCN） is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.