A method for combining molecular markers and phenotypic attributes for classifying plant genotypes

Classifying genotypes into clusters based on DNA fingerprinting, and/or agronomic attributes, for studying genetic and phenotypic diversity is a common practice. Researchers are interested in knowing the minimum number of fragments (and markers) needed for finding the underlying structural patterns of diversity in a population of interest, and using this information in conjunction with the phenotypic attributes to obtain more precise clusters of genotypes. The objectives of this study are to present: (1) a retrospective method of analysis for selecting a minimum number of fragments (and markers) from a study needed to produce the same classification of genotypes as that obtained using all the fragments (and markers), and (2) a classification strategy for genotypes that allows the combination of the minimum set of fragments with available phenotypic attributes. Results obtained on seven experimental data sets made up of different plant species, number of individuals per species’ and number of markers, showed that the retrospective analysis did indeed find few relevant fragments (and markers) that best discriminated the genotypes. In two data sets, the classification strategy of combining the information on the relevant minimum fragments with the available morpho-agronomic attributes produced compact and well-differentiated groups of genotypes.     

Individual identification from genetic marker data: developments and accuracy comparisons of methods

Genetic marker‐based identification of distinct individuals and recognition of duplicated individuals has important applications in many research areas in ecology, evolutionary biology, conservation biology and forensics. The widely applied genotype mismatch (MM) method, however, is inaccurate because it relies on a fixed and suboptimal threshold number (T_M) of mismatches, and often yields self‐inconsistent pairwise inferences. In this study, I improved MM method by calculating an optimal T_M to accommodate the number, mistyping rates, missing data and allele frequencies of the markers. I also developed a pairwise likelihood relationship (LR) method and a likelihood clustering (LC) method for individual identification, using poor‐quality data that may have high and variable rates of allelic dropouts and false alleles at genotyped loci. The 3 methods together with the relatedness (RL) method were then compared in accuracy by analysing an empirical frog data set and many simulated data sets generated under different parameter combinations. The analysis results showed that LC is generally one or two orders more accurate for individual identification than the other methods. Its accuracy is especially superior when the sampled multilocus genotypes have poor quality (i.e. teemed with genotyping errors and missing data) and highly replicated, a situation typical of noninvasive sampling used in estimating population size. Importantly, LC is the only method that guarantees to produce self‐consistent results by partitioning the entire set of multilocus genotypes into distinct clusters, each cluster containing one or more genotypes that all represent the same individual. The LC and LR methods were implemented in a computer program COLONY for free download from the Internet.     

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions

MOTIVATION: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk. RESULTS: While the original MDR method provides a simple binary measure of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group. AVAILABILITY: The program written in R is available.     

Multipoint Genetic Mapping with Trisomy Data

Trisomy is the most common genetic abnormality in humans and is the leading cause of mental retardation. Although molecular studies that use a large number of highly polymorphic markers have been undertaken to understand the recombination patterns for chromosome abnormalities, there is a lack of multilocus approaches to incorporating crossover interference in the analysis of human trisomy data. In the present article, we develop two statistical methods that simultaneously use all genetic information in trisomy data. The first approach relies on a general relationship between multilocus trisomy probabilities and multilocus ordered-tetrad probabilities. Under the assumption that no more than one chiasma exists in each marker interval, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses that lead to trisomy. One limitation of the first approach is that the amount of computation increases exponentially with the number of markers. The second approach models the crossover process as a chi(2) model. We describe how to use hidden Markov models to evaluate multilocus trisomy probabilities. Our methods are applicable when both parents are available or when only the nondisjoining parent is available. For both methods, genetic distances among a set of markers can be estimated and the pattern of overall chiasma distribution can be inspected for differences in recombination between meioses exhibiting trisomy and normal meioses. We illustrate the proposed approaches through their application to a set of trisomy 21 data.     

Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present

We present allelematch, an R package, to automate the identification of unique multilocus genotypes in data sets where the number of individuals is unknown, and where genotyping error and missing data may be present. Such conditions commonly occur in noninvasive sampling protocols. Output from the software enables a comparison of unique genotypes and their matches, and facilitates the review of differences between profiles. The software has a variety of applications in molecular ecology, and may be valuable where a large number of samples must be processed, unique genotypes identified, and repeated observations made over space and time. We used simulations to assess the performance of allelematch and found that it can reliably and accurately determine the correct number of unique genotypes (± 3%) across a broad range of data set properties. We found that the software performs with highest accuracy when genotyping error is below 4%. The R package is available from the Comprehensive R Archive Network (http://cran.r-project.org/). Supplementary documentation and tutorials are provided.     

Multiplexing of canine microsatellite markers for whole-genome screens

A set of 172 canine microsatellite markers, termed minimal screening set 1 (MSS1), was recently characterized for use in whole-genome screens. We report here the multiplexing of 155 MSS1 markers into 48 multiplex sets. Amplification of the multiplex sets is achieved using a single thermal cycling program. The markers are labeled with fluorescent dyes and optimized for resolution on an ABI 310 Genetic Analyzer or ABI 377 Sequencer. The multiplexing strategy involves amplifying combinations of markers so that no two markers with the same dye and product size overlap. Multiplexing the MSS1 provides an efficient tool for the collection of genotypes and streamlines whole-genome screens. Screening the canine genome for linkage of markers with various hereditary diseases facilitates identification of affected and carrier individuals, thereby providing researchers and clinicians with an additional diagnostic tool.     

hybridlab (version 1.0): a program for generating simulated hybrids from population samples

We present the computer program hybridlab 1.0 for simulating intraspecific hybrids from population samples of nuclear genetic markers such as microsatellites, allozymes or SNPs (single nucleotide polymorphisms). The program generates a user‐specified number of multilocus F1 hybrid genotypes between any pair of potentially hybridizing populations included in a standard input‐file of multilocus genotypes for population genetic analysis. This simple, user‐friendly program has a wide range of applications for studying natural and artificial hybridization; in particular, for evaluating the statistical power for individual assignment of parental and hybrid individuals. An example of application for Atlantic cod populations is given.     

An optimization method for the identification of minimal sets of discriminating gene markers: application to cultivar identification in wheat

A potentially large number of molecular markers are available for identifying genotypes in various species. For wheat, cultivar identity is an important determinant for end-use segregation and for payment of end-point royalties and grower premiums. A number of dominant DNA markers, that give either a positive or negative response, have been developed previously for wheat cultivar identification. This paper gives a method for identifying minimal marker sets for a given cultivar group, for example those grown in a specific geographical zone. It is based on an integer linear programming formulation of the problem, and can find all minimal marker sets for the group if required. The paper then describes the production of two software packages, GGDS and GGIP, that incorporate this methodology. Various practical issues are also discussed. These packages enable the rapid selection of minimal marker sets for the efficient discrimination of any sample set where the marker responses of the samples are known. They are already being used by the Australian wheat industry.     

Taking phylogenetics beyond pattern analysis: can models of genome dynamics guide predictions about homoplasy in morphological and behavioral data sets?

Despite the considerable amount of interest in phylogeny reconstruction, patterns of homoplasy in morphological and behavioral data have received only limited attention to date, whereas the patterns of homoplasy in molecular data are relatively well understood. First, because the number of alternative molecular character states is strictly limited (particularly for nucleotide sequence data), higher rates of substitution generate higher levels of homoplasy. Second, depending on the relative proportions of constrained and unconstrained sites, each molecular data set has a time frame of applicability outside of which resolution becomes ambiguous. There is good evidence to suggest that numbers of alternative character states for morphological and even behavioral data may be similarly limited and that higher rates of evolution are often linked to higher rates of homoplasy. Like molecular data sets, morphological and behavioral data sets contain rapidly evolving characters as well as more conservative elements. Morphologies and behaviors related to sexual recognition and reproduction show low levels of intraspecific variation, but high levels of lability between species, making them crucial for species identification but often poor as markers of relationship at greater time depths. The organization theory of speciation derived by Carson is a model based on genome dynamics, and it predicts exactly this window of applicability for characters related to sexual reproduction. Nonsexual characters related to environmental adaptation should be applicable at greater phylogenetic depths. A better understanding of patterns of homoplasy enables a more sophisticated approach to the assessment of the relative reliabilities of alternative tree topologies.     

clonality V.0.4: a randomization-based program to test for heterozygosity-genet size relationships in clonal organisms

clonality V.0.4 is a program for testing heterozygosity-genet size relationships in clonal organisms using a randomization procedure. The software has been developed under the Borland Delphi developing environment and a Windows-executable version is freely downloadable from http://gemi.mpl.ird.fr/SiteSGASS/Prugnolle/ClonalityPage.html. The program compares the observed F(IS) of the population with the F(IS) expected if genets (multilocus genotypes present in multiple copies within the population) were chosen randomly from the set of different multilocus genotypes. The randomization procedure is performed with the same number of genets and the same number of repetitions per genet as what is observed in the original data set.     

Formalizing reliability in the taxonomic congruence approach

In the 'total evidence' approach to phylogenetics, the reliability of a clade is implicitly measured by its degree of support, often embodied in a robustness index such as a bootstrap proportion. In the taxonomic congruence approach, the measurement of reliability has been implemented by various consensus or supertree methods, but was seldom explicitly discussed as such. We explore a reliability index for clades using their repetition across independent data sets. All possible combinations of the elementary data sets are used to compose the sets of independent data sets, across which the repetitions are counted. The more a clade occurs across such independent combinations, the higher its index. However, if other repeated clades occur that are incompatible with that clade, its index is decreased to take into account the uncertainty resulting from conflicting hypotheses. Results can be summarized through a greedy consensus tree in which clades appear according to their repetition indices. This index is tested on a 73 acanthomorph taxa data set composed of five independent molecular markers and multiple combinations of them. On this particular application, we confirm that reliability as defined here and robustness (estimated by bootstrap proportions obtained from a 'total evidence' approach) should be clearly distinguished.     

The impact of missing and erroneous genotypes on tagging SNP selection and power of subsequent association tests

OBJECTIVE: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. METHODS: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton's diversity based program htstep, Carlson's linkage disequilibrium (LD) based program ldSelect, and Stram's coefficient of determination based program tagsnp.exe. RESULTS: When randomly selected known loci were relabeled as 'missing', we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram's program had larger increase than Carlson's and Clayton's programs. In data sets simulated under the coalescent model, Carlson's program had the largest increase and Clayton's program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. CONCLUSIONS: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.     

Comparison of RAMP and SSR markers for the study of wild barley genetic diversity

Two molecular marker technologies, random amplified microsatellite polymorphism (RAMP) and simple sequence repeats (SSR), were used to determine genetic diversity of 27 accessions of the wild barley Hordeum vulgare ssp. spontaneum. 19 primer combinations were used to generate RAMP fragments and 16 SSR loci were analysed. A high level of polymorphism was found with both kind of markers as revealed by the mean polymorphism information content (PIC) values obtained: 0.838 and 0.855 for RAMP and SSR, respectively. Genetic dissimilarities between genotypes were estimated from RAMP and SSR data. A lack of correlation was found between both sets of data. This was reflected in the two dendrograms obtained which presented accessions clustered differently. The results suggest that both sets of markers reveal genetic variation induced by different mechanisms. The dendrogram produced from the RAMP dissimilarity estimates showed most of the groups related to the geographic origin of the accessions.     

An integrative approach to species discovery in odonates: from character‐based DNA barcoding to ecology

Modern taxonomy requires an analytical approach incorporating all lines of evidence into decision‐making. Such an approach can enhance both species identification and species discovery. The character‐based DNA barcode method provides a molecular data set that can be incorporated into classical taxonomic data such that the discovery of new species can be made in an analytical framework that includes multiple sources of data. We here illustrate such a corroborative framework in a dragonfly model system that permits the discovery of two new, but visually cryptic species. In the African dragonfly genus Trithemis three distinct genetic clusters can be detected which could not be identified by using classical taxonomic characters. In order to test the hypothesis of two new species, DNA‐barcodes from different sequence markers (ND1 and COI) were combined with morphological, ecological and biogeographic data sets. Phylogenetic analyses and incorporation of all data sets into a scheme called taxonomic circle highly supports the hypothesis of two new species. Our case study suggests an analytical approach to modern taxonomy that integrates data sets from different disciplines, thereby increasing the ease and reliability of both species discovery and species assignment.     

spads 1.0: a toolbox to perform spatial analyses on DNA sequence data sets

spads 1.0 (for ‘Spatial and Population Analysis of DNA Sequences’) is a population genetic toolbox for characterizing genetic variability within and among populations from DNA sequences. In view of the drastic increase in genetic information available through sequencing methods, spads was specifically designed to deal with multilocus data sets of DNA sequences. It computes several summary statistics from populations or groups of populations, performs input file conversions for other population genetic programs and implements locus‐by‐locus and multilocus versions of two clustering algorithms to study the genetic structure of populations. The toolbox also includes two Matlab and r functions, Gdispal and Gdivpal , to display differentiation and diversity patterns across landscapes. These functions aim to generate interpolating surfaces based on multilocus distance and diversity indices. In the case of multiple loci, such surfaces can represent a useful alternative to multiple pie charts maps traditionally used in phylogeography to represent the spatial distribution of genetic diversity. These coloured surfaces can also be used to compare different data sets or different diversity and/or distance measures estimated on the same data set.     

The evaluation for generic-level monophyly of Ancyrocephalinae (Monogenea, Dactylogyridae) using ribosomal DNA sequence data

To overcome the limited regional and taxonomic coverage in previous studies of the Ancyrocephalinae (Monogenea, Dactylogyridae), we present further findings on the molecular systematics and phylogeny of a broader selection of specimens, aiming to build a molecular phylogeny of the Ancyrocephalinae, and to assess the monophyly of each available genus, using D1-D2 domain of LSU rDNA and the combined LSU and partial sequence of SSU rDNA data sets. Our studies showed that 18 Haliotrema species were highly dispersive to form several clades with species from ten closely related genera. The host range of Euryhaliotrematoides species was not only restricted in butterfly fishes (Chaetodontidae), but widen to include the Lutjanidae. Euryhaliotrema was phylogenetically more closely related to Aliatrema than to Euryhaliotrematoides. Given that the species Haliotrema kurodai, Haliotrema spirotubiforum and Haliotrema anguiformis had a funnel-shaped base of the coiled male copulatory organ (MCO) lacking an accessory piece, and our molecular evidence, we proposed to transfer the three species to the Aliatrema as new combinations. We proposed to combine Bravohollisia and Caballeria into one genus, mainly based on molecular evidence and their similar MCO characters. Using SSU+ITS1 data set, Scutogyrus was robustly resolved as a polyphyletic group and its status should be questioned. Based on the present molecular evidence and their similar MCO characters, we proposed to combine Cichlidogyrus and Scutogyrus into one genus, Cichlidogyrus, by treating Scutogyrus as the synonym of Cichlidogyrus. Generally, the present study indicated that the vagina can make little contribution for understanding the generic-level monophyly, due to its high variability even among phylogenetically very closely related species, but it was useful for species determination. Given that phylogenetically closely related species from the same or closely related host species may have similar MCO characters but distinct haptoral characters, we consider that it is dangerous to erect a genus mainly based on different haptoral characters, particularly those separated from existing genera. The resultant phylogenetic reconstructions indicated that the radiation of some Haliotrema species has correlated with their hosts, because species from closely related fishes have correspondingly shown close relationships. Though the present study can not provide the comparison of host-parasite associations, phylogenetically closely related Haliotrema species which have similar morphological characters (both MCOs and haptors) but distinct/distantly related host species may indicate a speciation model of host-switching.     

polypatex: an r package for paternity exclusion in autopolyploids

Microsatellite markers have demonstrated their value for performing paternity exclusion and hence exploring mating patterns in plants and animals. Methodology is well established for diploid species, and several software packages exist for elucidating paternity in diploids; however, these issues are not so readily addressed in polyploids due to the increased complexity of the exclusion problem and a lack of available software. We introduce polypatex , an r package for paternity exclusion analysis using microsatellite data in autopolyploid, monoecious or dioecious/bisexual species with a ploidy of 4n, 6n or 8n. Given marker data for a set of offspring, their mothers and a set of candidate fathers, polypatex uses allele matching to exclude candidates whose marker alleles are incompatible with the alleles in each offspring–mother pair. polypatex can analyse marker data sets in which allele copy numbers are known (genotype data) or unknown (allelic phenotype data) – for data sets in which allele copy numbers are unknown, comparisons are made taking into account all possible genotypes that could arise from the compared allele sets. polypatex is a software tool that provides population geneticists with the ability to investigate the mating patterns of autopolyploids using paternity exclusion analysis on data from codominant markers having multiple alleles per locus.     

genclone: a computer program to analyse genotypic data,test for clonality and describe spatial clonal organization

genclone 1.0 is designed for studying clonality and its spatial components using genotype data with molecular markers from haploid or diploid organisms. genclone 1.0 performs the following tasks. (i) discriminates distinct multilocus genotypes (MLGs), and uses permutation and resampling approaches to test for the reliability of sets of loci and sampling units for estimating genotypic and genetic diversity (a procedure also useful for nonclonal organisms); (ii) computes statistics to test for clonal propagation or clonal identity of replicates; (iii) computes various indices describing genotypic diversity; and (iv) summarizes the spatial organization of MLGs with adapted spatial autocorrelation methods and clonal subrange estimates.     

Assessing parent numbers from offspring genotypes: the importance of marker polymorphism

Methods to infer parent numbers from offspring genotypes either determine the minimum number of parents required to explain alleles and multilocus genotypes detected in the offspring or use models to incorporate information on population allele frequencies and allele segregation. Disparate results by different approaches suggest that one or perhaps all methods are subject to bias. Here, we investigate the performance of minimum parent number estimates, maximum likelihood, and Bayesian analyses (programs COLONY and PARENTAGE) with respect to marker information content in simulated data sets without knowledge of parental genotypes. Offspring families of different sizes were assumed to share one parent and to be sired by 1 or 5 additional parents. All methods committed large errors in terms of underestimation (minimum value) and overestimation (COLONY), or both (PARENTAGE) of parent numbers, unless the data were highly informative, and their relative performances depended on full-sib group sizes and sire numbers. Increasing the number of markers with low gene diversity (H(e) < or = 0.68) yielded only slow improvement of the results, but all 3 methods performed well with 5-7 markers of H(e) = 0.84. We emphasize the importance of high marker polymorphism for inferring parent numbers and individual parent contributions, as well as for the detection of monogamous reproduction.     

Mapping loci influencing blood pressure in the Framingham pedigrees using model-free LOD score analysis of a quantitative trait

This paper presents a method of performing model-free LOD-score based linkage analysis on quantitative traits. It is implemented in the QMFLINK program. The method is used to perform a genome screen on the Framingham Heart Study data. A number of markers that show some support for linkage in our study coincide substantially with those implicated in other linkage studies of hypertension. Although the new method needs further testing on additional real and simulated data sets we can already say that it is straightforward to apply and may offer a useful complementary approach to previously available methods for the linkage analysis of quantitative traits.