SASQuant: a SAS software program to estimate genetic effects and heritabilities of quantitative traits in populations consisting of 6 related generations

Plant breeders are interested in the analysis of phenotypic data to measure genetic effects and heritability of quantitative traits and predict gain from selection. Measurement of phenotypic values of 6 related generations (parents, F(1), F(2), and backcrosses) allows for the simultaneous analysis of both Mendelian and quantitative traits. In 1997, Liu et al. released a SAS software based program (SASGENE) for the analysis of inheritance and linkage of qualitative traits. We have developed a new program (SASQuant) that estimates gene effects (Hayman's model), genetic variances, heritability, predicted gain from selection (Wright's and Warner's models), and number of effective factors (Wright's, Mather's, and Lande's models). SASQuant makes use of traditional genetic models and allows for their easy application to complex data sets. SASQuant is freely available and is intended for scientists studying quantitative traits in plant populations.     

bottlesim: a bottleneck simulation program for long‐lived species with overlapping generations

Population bottlenecks reduce genetic diversity and thus cause great concern in conservation biology. Previous theoretical studies often assume discrete generations in projecting declines in genetic diversity caused by bottlenecks. This assumption creates complexities when applying the models to long‐lived species with overlapping generations. bottlesim is a program for simulating bottlenecks to estimate the impact on genetic diversity; the novelties include an overlapping‐generation model, a wide range of reproductive systems, and flexible population size settings. With these features, bottlesim will be a useful tool for estimating the genetic consequences of bottlenecks, evaluating conservation plans, and performing power analysis.     

AQUASPLATCHE: a program to simulate genetic diversity in populations living in linear habitats

Classical models of structured populations do not apply well to species leaving in semilinear habitats such as freshwater fishes, since these habitats are intermediate between one‐dimensional and two‐dimensional stepping‐stone models. In order to investigate the genetic diversity of such populations, we have developed a new simulation program called aquasplatche . It starts by dividing a user‐defined vectorized network into segments of arbitrary length, each segment hosting a single deme. The program then proceeds by simulating the colonization of the environment from an arbitrary source, recording the evolution of the deme densities and the migration events between adjacent demes over time. This demographic history is then used to generate genetic data of population samples located in various segments of the network, using a backward coalescent framework. Different versions of aquasplatche are freely available on http://cmpg.unibe.ch/software/aquasplatche .     

Learning directed acyclic graphs from large-scale genomics data

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.     

Analysis of genes for alcoholism using two-disease-locus models

Using model-based two-locus methods for mapping genes, we analyzed the family data from the Collaborative Study on the Genetics of Alcoholism. Microsatellite data from 143 families ascertained through having three or more individuals affected with alcohol dependence were used for this investigation. Four regions showing evidence for linkage were identified using single-locus models from previous investigations. We investigated the genetic linkage, pattern of disease inheritance, and pair-wise genetic epistasis of these loci using the TLINKAGE program for two-disease-locus analysis.     

Mechanical competition alters the cellular interpretation of an endogenous genetic program

The intrinsic genetic program of a cell is not sufficient to explain all of the cell’s activities. External mechanical stimuli are increasingly recognized as determinants of cell behavior. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila, the genetic program of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells and thereby tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic program as their constricting neighbors. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress–strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with nonlinear mechanical properties do. Our models show that this behavior is a general emergent property of actomyosin networks in a supracellular context, in accordance with our experimental observations of actin reorganization within stretching cells.     

The case for genetic monitoring of mice and rats used in biomedical research

Currently, there is the potential to generate over 200,000 mutant mouse strains between existing mouse strains (over 24,000) and genetically modified mouse embryonic stem cells (over 209,000) that have been entered into the International Mouse Strain Resource Center (IMSR) from laboratories and repositories all over the world. The number of rat strains is also increasing exponentially. These mouse and rat mutants are a tremendous genetic resource; however, the awareness of their genetic integrity such as genetic background and genotyping of these models is not always carefully monitored. In this review, we make a case for the International Council for Laboratory Animal Science (ICLAS), which is interested in promoting and helping academic institutions develop a genetic monitoring program to bring a level of genetic quality assurance into the scientific interchange and use of mouse and rat genetically mutant models.     

Multiscale analysis of Hymenocallis coronaria (Amaryllidaceae) genetic diversity, genetic structure, and gene movement under the influence of unidirectional stream flow

Understanding gene movement patterns in unidirectional flow environments and their effect on patterns of genetic diversity and genetic structure is necessary to manage these systems. Hypotheses and models to explain genetic patterns in streams are rare, and the results of macrophyte studies are inconsistent. This study addresses Ritland's (Canadian Journal of Botany 67: 2017-2024) unidirectional diversity hypothesis, the one-dimensional stepping stone model, and the metapopulation model within and among populations. Hymenocallis coronaria, an aquatic macrophyte of rocky river shoals of the SE USA, was sampled in four river basins. Within populations and among populations <16.2 km apart had significant isolation by distance. However, the rate of gene flow decay was not consistent with a one-dimensional stepping stone model, nor was evidence strong or consistent for Ritland's hypothesis. Some evidence indicates that localized metapopulation processes may be affecting genetic diversity and structure; however, gene flow patterns inconsistent with the assumptions of the linear and unidirectional models are also a possible influence. We discuss three variants on the one-dimensional stepping stone model. Future research in linear environments should examine the expectations of these models. This study is also one of the first efforts to calculate population genetic parameters using a new program, TETRASAT.     

SLiM: Simulating Evolution with Selection and Linkage

SLiM is an efficient forward population genetic simulation designed for studying the effects of linkage and selection on a chromosome-wide scale. The program can incorporate complex scenarios of demography and population substructure, various models for selection and dominance of new mutations, arbitrary gene structure, and user-defined recombination maps.     

proseq: A software for preparation and evolutionary analysis of DNA sequence data sets

proseq is an integrated user‐friendly windows based program for convenient sequence editing and evolutionary analysis. It is designed to simplify preparation and analysis of DNA sequence data sets in population genetic, phylogenetic and molecular ecology studies. Sequence editor features include editing of chromatogram files, contig assembly, sequence alignment, translation and other utilities. Analysis features include calculation of genetic diversity, divergence, population subdivision and gene flow with permutation‐based significance testing and various tests of neutrality. A tool for coalescent simulations implements models with intragenic recombination, population subdivision and population growth.     

Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (<Emphasis Type="Italic">Juglans regia</Emphasis>)

Genetic components for economically important traits in walnut (Juglans regia) were estimated for the first time using historical pedigree and heirloom phenotypic data from the walnut breeding program at the University of California, Davis. The constructed pedigree is composed of ~ 15,000 individuals and is derived from current and historic phenotypic records dating back > 50 years and located across California. To predict the additive genetic values of individuals under selection, generalized linear mixed models (GLMM), implemented with MCMCglmm, were developed. Several repeatability models were established to obtain the best model and predict the genetic parameters for each trait. Repeatability for yield, harvest date, extra-light kernel color (ELKC), and lateral bearing were predicted at 0.82, 0.98, 0.63, and 0.96, respectively, and average narrow-sense heritabilities were 0.54, 0.77, 0.49, and 0.75, respectively. Each individual in the pedigree was ranked by its estimated breeding value (EBV). The genetic trend showed specific patterns for each trait, and real genetic improvement was found over time. The completed pedigree built here, the estimated breeding values, and the ranking of individuals according to their breeding values, can be used to guide future crossing designs in the walnut breeding program and future implementation of genomic selection methods in walnut.     

A framework for structural equation models in general pedigrees

Background/Aims: Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed. Methods: This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used. Results: A simulation study shows that our method is computationally feasible, and has good statistical properties. Conclusion: Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.     

A flexible forward simulator for populations subject to selection and demography

This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). AVAILABILITY: The source code (written in the C programming language) is available at http://sfscode.sourceforge.net, and a web server (http://cbsuapps.tc.cornell.edu/sfscode.aspx) allows the user to perform simulations using the high-performance computing cluster hosted by the Cornell University Computational Biology Service Unit.     

Mammalian prenatal development: the influence of maternally derived molecules

Normal fetal development is dependent upon an intricate exchange between mother and embryo. Several maternal and embryonic elements can influence this intimate interaction, including genetic, environmental or epigenetic factors, and have a significant impact on embryo development. The interaction of the genetic program of both mother and embryo, within the uterine environment, can shape the development of an individual. Accumulating data from animal models indicate that prenatal events may well initiate long‐term changes in the expression of the embryo genetic program, which persist, or may only become apparent, much later in the individual's life. Also, environmental conditions during prenatal development may prompt the adoption of different developmental pathways, leading to alternative life histories. This review focuses on environmental factors, specifically maternally derived molecules, to illustrate how they can influence in utero embryonic development and, by extension, adult life.     

PAQ: Partition Analysis of Quasispecies

MOTIVATION: The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. RESULTS: We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.     

Analytic approaches to twin data using structural equation models

The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these 'latent' factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.     

Ingeneue: a versatile tool for reconstituting genetic networks,with examples from the segment polarity network

Here we describe a software tool for synthesizing molecular genetic data into models of genetic networks. Our software program Ingeneue, written in Java, lets the user quickly turn a map of a genetic network into a dynamical model consisting of a set of ordinary differential equations. We developed Ingeneue as part of an ongoing effort to explore the design and evolvability of genetic networks. Ingeneue has three principal advantages over other available mathematical software: it automates instantiation of the same network model in each cell in a 2-D sheet of cells; it constructs model equations from pre-made building blocks corresponding to common biochemical processes; and it automates searches through parameter space, sensitivity analyses, and other common tasks. Here we discuss the structure of the software and some of the issues we have dealt with. We conclude with some examples of results we have achieved with Ingeneue for the Drosophila segment polarity network.     

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.     

pedagog: software for simulating eco-evolutionary population dynamics

pedagog is a Windows program that can be used to determine power for, and validate inferences drawn from, eco-evolutionary studies. It models dynamics of multiple populations and their interactions through individual-based simulations while simultaneously recording genotype, pedigree and trait information at the individual level. pedagog also allows for specification of heritable traits, natural and sexual selection acting upon those traits, population sampling schemes and incorporation of genetic and demographic errors into the output. Overall, parameters can be specified for genetic diversity, demographics, mating design, genetic and demographic errors, individual growth models, trait heritability and selection, and output formatting. Demographic parameters can be either age or function based, and all parameters can be drawn from 12 statistical distributions where appropriate. Simulation results can be automatically formatted for 57 existing software programs to facilitate postsimulation analyses. pedagog is freely available for download at https://bcrc.bio.umass.edu/pedigreesoftware/.