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PubMed2005

Modeling population genetic data in autotetraploid species.

Allozyme and PCR-based molecular markers have been widely used to investigate genetic diversity and population genetic structure in autotetraploid species. However, an empirical but inaccurate approach was often used to infer marker genotype from the pattern and intensity of gel bands. Obviously, this introduces serious errors in prediction of the marker genotypes and severely biases the data analysis. This article developed a theoretical model to characterize genetic segregation of alleles at genetic marker loci in autotetraploid populations and a novel likelihood-based method to estimate the model parameters. The model properly accounts for segregation complexities due to multiple alleles and double reduction at autotetrasomic loci in natural populations, and the method takes appropriate account of incomplete marker phenotype information with respect to genotype due to multiple-dosage allele segregation at marker loci in tetraploids. The theoretical analyses were validated by making use of a computer simulation study and their utility is demonstrated by analyzing microsatellite marker data collected from two populations of sycamore maple (Acer pseudoplatanus L.), an economically important autotetraploid tree species. Numerical analyses based on simulation data indicate that the model parameters can be adequately estimated and double reduction is detected with good power using reasonable sample size.

Alleles↗
PubMed1986

Statistical considerations for linkage analysis using recombinant inbred strains and backcrosses.

Recombinant inbred (RI) mouse strains are extremely useful for gene mapping, especially for establishing preliminary map positions for new loci. However, the usual statistical analysis applied to such experiments may lead to erroneous conclusions about linkage unless unusually stringent criteria are adopted for rejecting the null hypothesis. We describe a Bayesian statistical approach for determining the probability of linkage when no prior information is available about the location of the gene to be mapped (the test locus). We present a table that gives the probability of linkage, the most likely position of the test locus with respect to a marker locus, and the interval around the marker locus that has a 95% chance of containing the test locus, for all possible experimental results suggesting linkage in sets of up to 40 RI strains. These results show that for the probability of linkage to be greater than 95%, the number of RI strains inheriting chromosomes recombinant for the test and marker loci must be smaller than previously assumed. The formulas derived for RI strains can be applied, with only minor modifications, to the analysis of Mendelian backcrosses. Differences between the Bayesian approach advocated here and the more traditional analysis of linkage are discussed in detail.

Animals↗
PubMed2003

Family-based association analysis with tightly linked markers.

Refining genomic regions which have been identified by linkage analysis to contain a disease susceptibility locus has proven to be a challenging task. Detecting association between the disease and a genetic marker can significantly narrow down the candidate region. Since an adequate sample of families is already available from the genome scan, family-based association tests may be used to search for association. The use of haplotypes consisting of tightly linked markers can be more powerful for detecting association than the use of individual markers. An extension of the transmission/disequilibrium test to allow the simultaneous analysis of more than one marker locus is complicated by ambiguity of phase in some families of the sample. The present paper shows that a recently proposed method for the analysis of nuclear families with a single affected child can be viewed as a special application of a more general principle. This observation justifies several modifications, potentially increasing the power, as well as an extension of the method to allow the analysis of general nuclear families. Finally, the problem of missing parental genotypes is discussed.

Chromosome Mapping↗
PubMed2002

Reducing environmental bias when measuring natural selection.

Crucial to understanding the process of natural selection is characterizing phenotypic selection. Measures of phenotypic selection can be biased by environmental variation among individuals that causes a spurious correlation between a trait and fitness. One solution is analyzing genotypic data, rather than phenotypic data. Genotypic data, however, are difficult to gather, can be gathered from few species, and typically have low statistical power. Environmental correlations may act through traits other than through fitness itself. A path analytic framework, which includes measures of such traits, may reduce environmental bias in estimates of selection coefficients. We tested the efficacy of path analysis to reduce bias by re-analyzing three experiments where both phenotypic and genotypic data were available. All three consisted of plant species (Impatiens capensis, Arabidopsis thaliana, and Raphanus sativus) grown in experimental plots or the greenhouse. We found that selection coefficients estimated by path analysis using phenotypic data were highly correlated with those based on genotypic data with little systematic bias in estimating the strength of selection. Although not a panacea, using path analysis can substantially reduce environmental biases in estimates of selection coefficients. Such confidence in phenotypic selection estimates is critical for progress in the study of natural selection.

Arabidopsis↗
PubMed1989

Statistical tests for detecting gene conversion.

Statistical tests for detecting gene conversion are described for a sample of homologous DNA sequences. The tests are based on imbalances in the distribution of segments on which some pair of sequences agrees. The methods automatically control for variable mutation rates along the genome and do not depend on a priori choices of potentially monophyletic subsets of the sample. The tests show strong evidence for multiple intragenic conversion events at two loci in Escherichia coli. The gnd locus in E. coli shows a highly significant excess of maximal segments of length 70-200 bp, which suggests conversion events of that size. The data also indicate that the rate of these short conversion events might be of the order of neutral mutation rate. There is also evidence for correlated mutation in adjacent codon positions. The same tests applied to a locus in an RNA virus were negative.

Amino Acid Sequence↗
PubMed2003

Fine mapping of complex trait genes combining pedigree and linkage disequilibrium information: a Bayesian unified framework.

We present a Bayesian method that combines linkage and linkage disequilibrium (LDL) information for quantitative trait locus (QTL) mapping. This method uses jointly all marker information (haplotypes) and all available pedigree information; i.e., it is not restricted to any specific experimental design and it is not required that phases are known. Infinitesimal genetic effects or environmental noise ("fixed") effects can equally be fitted. A diallelic QTL is assumed and both additive and dominant effects can be estimated. We have implemented a combined Gibbs/Metropolis-Hastings sampling to obtain the marginal posterior distributions of the parameters of interest. We have also implemented a Bayesian variant of usual disequilibrium measures like D' and r(2) between QTL and markers. We illustrate the method with simulated data in "simple" (two-generation full-sib families) and "complex" (four-generation) pedigrees. We compared the estimates with and without using linkage disequilibrium information. In general, using LDL resulted in estimates of QTL position that were much better than linkage-only estimates when there was complete disequilibrium between the mutant QTL allele and the marker. This advantage, however, decreased when the association was only partial. In all cases, additive and dominant effects were estimated accurately either with or without disequilibrium information.

Bayes Theorem↗
PubMed2002

Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms.

The purpose of this work is to quantify the effects that errors in genotyping have on power and the sample size necessary to maintain constant asymptotic Type I and Type II error rates (SSN) for case-control genetic association studies between a disease phenotype and a di-allelic marker locus, for example a single nucleotide polymorphism (SNP) locus. We consider the effects of three published models of genotyping errors on the chi-square test for independence in the 2 x 3 table. After specifying genotype frequencies for the marker locus conditional on disease status and error model in both a genetic model-based and a genetic model-free framework, we compute the asymptotic power to detect association through specification of the test's non-centrality parameter. This parameter determines the functional dependence of SSN on the genotyping error rates. Additionally, we study the dependence of SSN on linkage disequilibrium (LD), marker allele frequencies, and genotyping error rates for a dominant disease model. Increased genotyping error rate requires a larger SSN. Every 1% increase in sum of genotyping error rates requires that both case and control SSN be increased by 2-8%, with the extent of increase dependent upon the error model. For the dominant disease model, SSN is a nonlinear function of LD and genotyping error rate, with greater SSN for lower LD and higher genotyping error rate. The combination of lower LD and higher genotyping error rates requires a larger SSN than the sum of the SSN for the lower LD and for the higher genotyping error rate.

Case-Control Studies↗
PubMed1993

The application of jackknife statistics to estimates of the recombination fraction.

We develop and evaluate the jackknife statistics [Efron, 1982] for obtaining confidence intervals for the recombination fraction. We consider two cases: (1) a single sibship of size S with phase known parents (one doubly heterozygous and one doubly homozygous) and (2) a sample of 20 nuclear families. We compare the jackknife confidence interval to the -1.00 lod and -0.83 lod intervals. For the first case we compare our intervals with a confidence interval which we develop that has coverage of exactly 95%. For the second case, we do a simulation study and compare the coverage of the intervals and the endpoints of the intervals with the actual 2.5th and 97.5th percentiles. Our results indicate that in case (1) the lod intervals provide closer estimates to the 95% exact interval than does the jackknife approach. However, in case (2), although the lod intervals have better coverage probabilities, the jackknife interval endpoints are closer to the actual percentile points than either of the lod interval endpoints.

Genetic Diseases, Inborn↗
PubMed2006

Assessing genetic diversity and population structure in a citrus germplasm collection utilizing simple sequence repeat markers (SSRs).

Twenty-four simple sequence repeat (SSR) markers were used to detect molecular polymorphisms among 370 mostly sexually derived Citrus accessions from the collection of citrus germplasm maintained at the University of California, Riverside. A total of 275 alleles were detected with an average of 11.5 alleles per locus and an average polymorphism information content of 0.625. Genetic diversity statistics were calculated for each individual SSR marker, the entire population, and for specified Citrus groups. Phylogenetic relationships among all citrus accessions and putative non-hybrid Citrus accessions were determined by constructing neighbor-joining trees. There was strong support for monophyly at the species level when hybrid taxa were removed from the data set. Both of these trees indicate that Fortunella clusters within the genus Citrus but Poncirus is a sister genus to Citrus. Additionally, Citrus accessions were probabilistically assigned to populations or multiple populations if their genotype indicated an admixture by a model-based clustering approach. This approach identified five populations in this data set. These separate analyses (distance and model based) both support the hypothesis that there are only a few naturally occurring species of Citrus and most other types of Citrus arose through various hybridization events between these naturally occurring forms.

Alleles↗
PubMed2003

A diffusion approximation for selection and drift in a subdivided population.

The population-genetic consequences of population structure are of great interest and have been studied extensively. An area of particular interest is the interaction among population structure, natural selection, and genetic drift. At first glance, different results in this area give very different impressions of the effect of population subdivision on effective population size (N(e)), suggesting that no single value of N(e) can completely characterize a structured population. Results presented here show that a population conforming to Wright's island model of subdivision with genic selection can be related to an idealized panmictic population (a Wright-Fisher population). This equivalent panmictic population has a larger size than the actual population; i.e., N(e) is larger than the actual population size, as expected from many results for this type of population structure. The selection coefficient in the equivalent panmictic population, referred to here as the effective selection coefficient (s(e)), is smaller than the actual selection coefficient (s). This explains how the fixation probability of a selected allele can be unaffected by population subdivision despite the fact that subdivision increases N(e), for the product N(e)s(e) is not altered by subdivision.

Computer Simulation↗
PubMed2003

Comparing G matrices: are common principal components informative?

Common principal components (CPC) analysis is a technique for assessing whether variance-covariance matrices from different populations have similar structure. One potential application is to compare additive genetic variance-covariance matrices, G. In this article, the conditions under which G matrices are expected to have common PCs are derived for a two-locus, two-allele model and the model of constrained pleiotropy. The theory demonstrates that whether G matrices are expected to have common PCs is largely determined by whether pleiotropic effects have a modular organization. If two (or more) populations have modules and these modules have the same direction, the G matrices have a common PC, regardless of allele frequencies. In the absence of modules, common PCs exist only for very restricted combinations of allele frequencies. Together, these two results imply that, when populations are evolving, common PCs are expected only when the populations have modules in common. These results have two implications: (1) In general, G matrices will not have common PCs, and (2) when they do, these PCs indicate common modular organization. The interpretation of common PCs identified for estimates of G matrices is discussed in light of these results.

Data Interpretation, Statistical↗
PubMed2005

The distribution of fitness effects among beneficial mutations in Fisher's geometric model of adaptation.

Recent models of adaptation at the DNA sequence level assume that the fitness effects of new mutations show certain statistical properties. In particular, these models assume that the distribution of fitness effects among new mutations is in the domain of attraction of the so-called Gumbel-type extreme value distribution. This assumption has not, however, been justified on any biological or theoretical grounds. In this note, I study random mutation in one of the simplest models of mutation and adaptation-Fisher's geometric model. I show that random mutation in this model yields a distribution of mutational effects that belongs to the Gumbel type. I also show that the distribution of fitness effects among rare beneficial mutations in Fisher's model is asymptotically exponential. I confirm these analytic findings with exact computer simulations. These results provide some support for the use of Gumbel-type extreme value theory in studies of adaptation and point to a surprising connection between recent phenotypic- and sequence-based models of adaptation: in both, the distribution of fitness effects among rare beneficial mutations is approximately exponential.

Adaptation, Physiological↗
PubMed2003

Genetic diversity and population history of golden monkeys (Rhinopithecus roxellana).

Golden monkey (Rhinopithecus roxellana), namely the snub-nosed monkey, is a well-known endangered primate, which distributes only in the central part of mainland China. As an effort to understand the current genetic status as well as population history of this species, we collected a sample of 32 individuals from four different regions, which cover the major habitat of this species. Forty-four allozyme loci were surveyed in our study by allozyme electrophoresis, none of which was found to be polymorphic. The void of polymorphism compared with that of other nonhuman primates is surprising particularly considering that the current population size is many times larger than that of some other endangered species. Since many independent loci are surveyed in this study, the most plausible explanation for our observation is that the population has experienced a recent bottleneck. We used a coalescent approach to explore various scenarios of population bottleneck and concluded that the most recent bottleneck could have happened within the last 15,000 years. Moreover, the proposed simulation approach could be useful to researchers who need to analyze the non- or low-polymorphism data.

Animals↗
PubMed2005

Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix.

Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an 'effective number' (M(eff)) of independent tests. However, our experience has indicated that Cheverud's estimate of the Meff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the M(eff), and design M(eff)-based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the M(eff)-based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the M(eff) is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the M(eff) method provides an alternative to computationally intensive methods such as the permutation test.

Data Interpretation, Statistical↗
PubMed2001

Recombination within natural populations of pathogenic bacteria: short-term empirical estimates and long-term phylogenetic consequences.

The identification of clones within bacterial populations is often taken as evidence for a low rate of recombination, but the validity of this inference is rarely examined. We have used statistical tests of congruence between gene trees to examine the extent and significance of recombination in six bacterial pathogens. For Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus pyogenes, and Staphylococcus aureus, the congruence between the maximum likelihood trees reconstructed using seven house-keeping genes was in most cases no better than that between each tree and trees of random topology. The lack of congruence between gene trees in these four species, which include both naturally transformable and nontransformable species, is in three cases supported by high ratios of recombination to point mutation during clonal diversification (estimates of this parameter were not possible for Strep. pyogenes). In contrast, gene trees constructed for Hemophilus influenzae and pathogenic isolates of Escherichia coli showed a higher degree of congruence, suggesting lower rates of recombination. The impact of recombination therefore varies between bacterial species but in many species is sufficient to obliterate the phylogenetic signal in gene trees.

Alleles↗
PubMed1992

The genotoxicity of the anti-cancer drug mitoxantrone in somatic and germ cells of Drosophila melanogaster.

The novel antineoplastic drug mitoxantrone was studied for its genotoxic effects in Drosophila melanogaster. In male germ cells, the clinical preparation Novantrone, the dihydrochloride salt of mitoxantrone, did not induce sex-linked recessive lethal mutations in feeding and injection experiments with adult flies, although statistically the results were inconclusive rather than truly negative. However, the free base mitoxantrone was weakly, but significantly genotoxic in this test (0.14% lethals/mM exposure concentration); this is most probably the result of prolonged exposure. On the other hand, both forms of mitoxantrone assayed were clearly genotoxic in the somatic mutation and recombination test of the wing. This test assays the cells of the proliferating imaginal wing discs of larvae. Depending on the feeding method used, the overall clone induction frequency was in the range of about 2-6 x 10(-5) per cell and cell generation and per mM exposure dose. Correction of these frequencies according to mean clone size led to slightly higher estimates (by about 5-25% higher). Although the majority of the clone induction events are due to mitotic recombination, a significant proportion can be attributed to mutational events (gene and chromosome mutations). The genotoxicity of mitoxantrone seems to depend mainly on impaired DNA synthesis in cycling cells owing to the compound's ability to inhibit topoisomerase II by intercalation into DNA.

Animals↗
PubMed2003

Population genetic structure of the toad Bufo woodhousii: an empirical assessment of the effects of haplotype extinction on nested cladistic analysis.

Nested cladistic analysis (NCA) is increasingly being used to infer historical population-level processes, including population fragmentation, range expansion and long-distance colonization. However, the effects on interpretation of NCA inferences of stochastic extinction of haplotypes due to genetic drift (lineage sorting), or of haplotype loss via localized biotic or climatic influences, have not been thoroughly explored. We provide empirical evidence suggesting that NCA may misinterpret population history when haplotypes or haplotype groups from one clade are replaced by those of another clade. We do so by using NCA to analyse mitochondrial sequences from the toad Bufo woodhousii from 45 locations spanning the Great Plains and southwestern USA. Portions of this region were glaciated and/or desertified in the late Pleistocene and early Holocene, and hence uninhabitable for plains-dwelling organisms. Although NCA inferences of isolation-by-distance and gradual range expansion in B. woodhousii are compatible with expectations based on climatic data and toad biology, NCA also detected several instances of long-distance movement. Such movement seems unlikely, given the low vagility of this species. We conclude that inferences of long-distance colonization likely result from extinction of haplotypes in intervening areas. We suggest using additional methods to look for congruent inferences, and amending the NCA inference key, to help avoid misinterpretations resulting from haplotype extinction.

Animals↗
PubMed1999

Phylogeographical autocorrelation of phenotypic evolution in honey bees (Apis mellifera L.).

The analysis of phenotypic divergence among local populations within a species has been traditionally performed in a spatial context, although advances in genetic analysis using mtDNA have permitted a simultaneous evaluation of geographical and historical patterns of variation, so-called phylogeographical analysis. In this paper, we combine these two dimensions of variation (geographical space and phylogenetic history) to evaluate patterns of phenotypic evolution in honey bees (Apis mellifera L.). Data on 39 phenotypic traits, derived from 417 colonies grouped into 14 subspecies, were analysed using autocorrelation methods. Mantel tests indicated that the relationship between phenotypic divergence, estimated by Euclidean distances among subspecies' morphological centroids, was significant both when compared to geographical distance (r=0.371; P < 0.01) and to genetic distance (estimated as sequence divergence (%) in a mtDNA region encompassing part of the NADH dehydrogenase subunit 2 and isoleucine transfer RNA (r=0.329; P < 0.01)). For the analysis of each trait, the effects of the geographical co-ordinates (latitude and longitude of subspecies geographical range) and of the phylogenetic patterns (defined by eigenvectors of the genetic distance matrix) on phenotypic variation were simultaneously analysed using an extension of a recently developed model, called Phylogenetic Eigenvector Regression (PVR). In general terms, the partial regression slopes indicated that the variation in the characters traditionally associated with adaptive processes, such as body and wing size, were better explained by geographical position. However, characters usually thought to be neutral, such as wing venation angle, were more associated with phylogeny. This is expected because PVR can be interpreted as a partition model, in which adaptive variation tends to be independent of phylogeny (and, in this case, associated with geography). In addition, the first principal component derived from the expected values of the model for each trait, which can be interpreted as the phenotypic variation predicted by phylogeny, is more structured in a north-south cline than are the original data, supporting an adaptive interpretation. The phylogeographical autocorrelation analyses performed in this study show that different traits are more related to one of the two dimensions of variation (geography and phylogeny), and these patterns can furnish insights into the nature of phenotypic evolution in these organisms.

Animals↗