POPSIM: a general population simulation program

SNP-based analysis reveals unexpected features of genetic diversity, parental contributions and pollen contamination in a white spruce breeding program

Accurate monitoring of genetic diversity levels of seedlots and mating patterns of parents from seed orchards are crucial to ensure that tree breeding programs are long-lasting and will deliver anticipated genetic gains. We used SNP genotyping to characterize founder trees, five bulk seed orchard seedlots, and trees from progeny trials to assess pollen contamination and the impact of severe roguing on genetic diversity and parental contributions in a first-generation open-pollinated white spruce clonal seed orchard. After severe roguing (eliminating 65% of the seed orchard trees), we found a slight reduction in the Shannon Index and a slightly negative inbreeding coefficient, but a sharp decrease in effective population size (eightfold) concomitant with sharp increase in coancestry (eightfold). Pedigree reconstruction showed unequal parental contributions across years with pollen contamination levels between 12 and 51% (average 27%) among seedlots, and 7-68% (average 30%) among individual genotypes within a seedlot. These contamination levels were not correlated with estimates obtained using pollen flight traps. Levels of pollen contamination also showed a Pearson's correlation of 0.92 with wind direction, likely from a pollen source 1 km away from the orchard under study. The achievement of 5% genetic gain in height at rotation through eliminating two-thirds of the orchard thus generated a loss in genetic diversity as determined by the reduction in effective population size. The use of genomic profiles revealed the considerable impact of roguing on genetic diversity, and pedigree reconstruction of full-sib families showed the unanticipated impact of pollen contamination from a previously unconsidered source.

gesp: A computer program for modelling genetic effective population size, inbreeding and divergence in substructured populations

The genetically effective population size (N_e) is of key importance for quantifying rates of inbreeding and genetic drift and is often used in conservation management to set targets for genetic viability. The concept was developed for single, isolated populations and the mathematical means for analysing the expected N_e in complex, subdivided populations have previously not been available. We recently developed such analytical theory and central parts of that work have now been incorporated into a freely available software tool presented here. gesp (Genetic Effective population size, inbreeding and divergence in Substructured Populations) is R‐based and designed to model short‐ and long‐term patterns of genetic differentiation and effective population size of subdivided populations. The algorithms performed by gesp allow exact computation of global and local inbreeding and eigenvalue effective population size, predictions of genetic divergence among populations (G_ST) as well as departures from random mating (F_IS, F_IT) while varying (i) subpopulation census and effective size, separately or including trend of the global population size, (ii) rate and direction of migration between all pairs of subpopulations, (iii) degree of relatedness and divergence among subpopulations, (iv) ploidy (haploid or diploid) and (v) degree of selfing. Here, we describe gesp and exemplify its use in conservation genetics modelling.

Genetic data simulators and their applications: an overview

Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators.

Efficient simulation of epistatic interactions in case-parent trios

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

Non-random mating involving inheritance of social status

We propose a unified theoretical scheme to calculate genotype frequencies of polymorphic populations with biological and social inheritance. This scheme is used to simulate the effects of social non-random mating on the population's evolution in the general situation of multiple biological gene loci and allele inheritance with natural selection and mutation. We study the case of hemophilia in several diverse situations when there exists non-random mating due to inheritance of social status. This study clearly shows that interaction between the social and the biological gene has significant impact on the population's evolution.

“Population Lab”: The Creation of Virtual Populations for Genetic Epidemiology Research

BACKGROUND

Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models, changing demographics and incidence, and incompleteness of the data. Furthermore, there are no standard tools for testing the validity of estimates from standard epidemiologic designs and from new analytic strategies using register data.

METHODS

We present a method and a software package for simulating realistic populations of related individuals, using easily available vital statistics (population counts and fertility and mortality rates). The virtual population is stored in a pedigree file, allowing for easy retrieval of relatives and family structures. We simulate breast cancer in our population using age-specific incidence rates.

RESULTS

The Swedish population is simulated as dynamically evolving over the calendar period 1955-2002. The simulated and real population agree well on important features such as age profile, sibship size distribution, and average age at first birth. Using breast cancer as an example, we present several models of familial disease aggregation and show that the parameters used in the simulations are faithfully estimated. In addition, we illustrate how our simulated population provides insight into how incomplete family history in real register data can affect estimates of familial risk.

CONCLUSIONS

This simulation method can be used to investigate various underlying models of disease aggregation in families and enhance the development of optimal approaches for family studies. The software package, Population Lab, is available for free download (http://www.meb.ki.se/ approximately marrei/software/poplab/ and http://cran.at.r-project.org/).

Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data

Genome wide association studies using high throughput technology are already being conducted despite the significant hurdles that need to be overcome (Nat Rev Genet 6:95-108, 2005; Nat Rev Genet 6:109-118, 2005). Methods for detecting haplotype association signals in genome wide haplotype datasets are as yet very limited. Much methodological research has already been devoted to linkage disequilibrium (LD) fine mapping where the focus is the identification of the disease locus rather than the detection of a disease signal. Applications of these approaches to genome wide scanning are limited by the strong model assumptions of the sharing process, which lead to computational complexity. We describe a new algorithm for the initial identification of disease susceptibility loci in genome wide haplotype association studies. Excess sharing of ancestral haplotypes, which indicates the presence of a disease locus, is detected with a simple, easy to interpret, chi2 based statistic. The method allows genome wide scanning for qualitative traits within reasonable computational timeframes and can serve as a first pass analysis prior to the usage of likelihood based methods, providing candidate regions and inferred susceptibility haplotypes. Our method makes no assumptions regarding the population history or the pattern of background LD. Statistical significance is evaluated with permutation tests. The method is illustrated on simulated and real data where it is applied to simple (cystic fibrosis) and complex disease (multiple sclerosis) examples. The statistic has low type I error and greater power to map disease loci over conventional single marker tests for low to moderate levels of LD.

Backward simulation of ancestors of sampled individuals

If the population is large and the sampling mechanism is random, the coalescent is commonly used to model the haplotypes in the sample. Ordered genotypes can then be formed by random matching of the derived haplotypes. However, this approach is not realistic when (1) there is departure from random mating (e.g., dominant individuals in breeding populations or monogamy in humans), or (2) the population is small and/or the individuals in the sample are ascertained by applying some particular non-random sampling scheme, as is usually the case when considering the statistical modeling and analysis of pedigree data. For such situations, we present here a data generation method where an ancestral graph with non-overlapping generations is first generated backwards in time, using ideas from coalescent theory. Alleles are randomly assigned to the founders, and subsequently the gene flow over the entire genome is simulated forwards in time by dropping alleles down the graph according to recombination model without interference. The parameters controlling the mating behavior of generated individuals in the graph (degree of monogamy) can be tuned in order to match a particular demographic situation, without restriction to simple random mating. The performance of the approach is illustrated with a simulation example. The software (written in C-language) is freely available for research purposes at http://www.rni.helsinki.fi/~dag/.