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At least 1,333 records · Page 74Linked to original sources
PubMed2001

Statistical analysis of uniparental disomy data using hidden Markov models.

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.

Biometry↗
PubMed2002

Contrasting models for lactation curve analysis.

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.

Animals↗
PubMed2006

Simple allelic-phenotype diversity and differentiation statistics for allopolyploids.

The analysis of genetic diversity within and between populations is a routine task in the study of diploid organisms. However, population genetic studies of polyploid organisms have been hampered by difficulties associated with scoring and interpreting molecular data. This occurs because the presence of multiple alleles at each locus often precludes the measurement of genotype or allele frequencies. In allopolyploids, the problem is compounded because genetically distinct isoloci frequently share alleles. As a result, analysis of genetic diversity patterns in allopolyploids has tended to rely on the interpretation of phenotype frequencies, which loses information available from allele composition. Here, we propose the use of a simple allelic-phenotype diversity statistic (H') that measures diversity as the average number of alleles by which pairs of individuals differ. This statistic can be extended to a population differentiation measure (F'ST), which is analogous to FST. We illustrate the behaviour of these statistics using coalescent computer simulations that show that F'ST behaves in a qualitatively similar way to FST, thus providing a useful way to quantify population differentiation in allopolyploid species.

Alleles↗
PubMed1999

Genetic diversity in peripheral and subcentral populations of corrigiola litoralis L. (Illecebraceae)

Genetic diversity and differentiation were studied in Corrigiola litoralis L., an annual plant species growing on seasonally flooded river banks. Plant species that are restricted to river systems may consist of highly isolated populations. For this species, pronounced genetic differentiation among rivers was expected. Plants were sampled from the river Loire (France) representing subcentral populations and the rivers Rhine, Weser and Elbe (Germany) representing peripheral ones. Allozyme electrophoresis revealed 17 putative loci in 11 enzyme systems. At the species level, percentage polymorphic loci, mean number of alleles, observed heterozygosity and expected heterozygosity were P=29%, A=1.5 +/- 0.2, Ho=0.007 +/- 0.005 and He=0.065 +/- 0.035, respectively. Peripheral populations were smaller in number and showed decreased levels of genetic diversity relative to central populations. Corrigiola litoralis was highly inbreeding as indicated by a mean FIS of 0.755. Genetic differentiation among populations was high with a mean FST-value of 0.585. Hierarchical F-statistics revealed that genetic variability was partitioned at 57% among sites, 52% among countries and 11% among sites within countries. Genetic distances between French and German populations were 0.08, indicative of considerable differentiation at the intraspecific level. The overall low level of allozyme diversity is attributed to the breeding system and to habitat conditions homogenized by regular flooding. The decrease in diversity from subcentral to peripheral populations is considered to be a result of drift and founder effects during postglacial recolonization. Peripheral populations were characterized by a single fixed allele at locus IDH, thus representing an evolutionarily significant unit.

Journal Article↗
PubMed2005

[How about the uncertainty in the haplotypes in the population-based KORA studies?].

In the KORA surveys, numerous candidate genes in the context of type 2 diabetes, myocardial infarction, atherosclerosis or obesity are under investigation. Current focus is on genotyping single nucleotide polymorphism (SNPs). Haplotypes are also of increasing interest: haplotypes are combinations of alleles within a certain section of one chromosome. Analysing haplotypes in genetic association studies is often more efficient than studying the SNPs separately. A statistical problem in this context is the reconstruction of the phase: genotyping the SNPs determines the alleles of an individual at one particular locus of the DNA, but does not reveal which allele is located on which one of the two chromosomes. This information is required when talking about haplotypes. There are statistical approaches to identify the most likely two haplotypes of an individual given the genotypes. However, a certain error in prognosis is unavoidable. There are also errors in the genotypes. These errors are assumed to be small for one SNP but can accumulate over the SNPs involved in one haplotype and thus can induce further uncertainty in the haplotype. It is therefore the aim of our project to quantify the uncertainties in the haplotypes particularly for genes investigated in the KORA surveys. We conduct computer simulations based on the haplotypes and their frequencies observed in the KORA individuals and compare the results with simulations based on mathematical modelling of the evolutionary process ("coalescent models"). The uncertainties in the haplotypes have an impact on the search for association between genes and disease: an association may not be detected as the haplotype uncertainty obscures the haplotype frequency differences between cases and controls. It is a further aim of our project to elucidate the extent of this problem and to develop strategies for reducing it.

Adult↗
PubMed1999

Bioinvasions of the medfly Ceratitis capitata: source estimation using DNA sequences at multiple intron loci.

The Mediterranean fruit fly, Ceratitis capitata, is a devastating agricultural pest that threatens to become established in vulnerable areas such as California and Florida. Considerable controversy surrounds the status of Californian medfly infestations: Do they represent repeated introductions or the persistence of a resident population? Attempts to resolve this question using traditional population genetic markers and statistical methods are problematic because the most likely source populations in Latin America were themselves only recently colonized and are genetically very similar. Here, significant population structure among several New World medfly populations is demonstrated through the analysis of DNA sequence variation at four intron loci. Surprisingly, in these newly founded populations, estimates of population structure increase when measures of subdivision take into account the relatedness of alleles as well as their frequency. A nonequilibrium, likelihood-based statistical test that utilizes multilocus genotypes suggests that the sole medfly captured in California during 1996 was introduced from Latin America and was less likely to be a remnant of an ancestral Californian population. Many bioinvasions are hierarchical in nature, consisting of several sequential or overlapping invasion events, the totality of which can be termed a metainvasion. Phylogenetic data from multilocus DNA sequences will be vital to understanding the evolutionary and ecological processes that underlie metainvasions and to resolving their constituent levels.

Alleles↗
PubMed2001

A non-parametric approach to translating gene region heterogeneity associated with phenotype into location heterogeneity.

MOTIVATION: The analysis of genetic data poses statistical problems in the form of high dimensionality with small sample sizes. The construction of a composite gene region (sequence pair) heterogeneity measure is one technique for reducing the dimensionality of the problem. This approach however is not without cost, since the contribution of locations to observed gene region differences between groups becomes entangled in this summary measure. This is problematic since it is of scientific interest to identify locations that together depict phenotype. RESULTS: A method is proposed for relating observed gene region heterogeneity back to the location level. In the spirit of a factor analysis-type setting, the approach focuses on identifying a latent variable structure among locations to explain within and between group genetic differences associated with phenotype. The method is flexible for identifying either the additive contribution from individual locations or the additive contribution from a group of locations, to observed gene region heterogeneity, depending upon the weighting scheme used in constructing a gene region heterogeneity measure. The approach is illustrated with clinical trial data, where the problem of altered HIV drug susceptibility is examined through characterizing location contributions to HIV protease gene region differences associated with a phenotypic treatment response. AVAILABILITY: The Splus (MathSoft, Inc. S-Plus 2000, Seattle, WA, 1999) developed menu-driven functions for obtaining results, GENE_ S (J.Kowalski, Harvard School of Public Health, Boston, MA 2001), is available from the author upon request.

Clinical Trials, Phase II as Topic↗
PubMed1980

Comparing the "family set" approach to estimate heritability with other conventional estimators: a Monte Carlo simulation.

A Monte Carlo simulation is performed to study the distribution of the family set estimators of heritability and to compare their performance with that of some conventional estimators used in human genetics. Comparisons of statistical properties among the two family set estimators (one based on the partial regression coefficients of the index's values on those of sib and cousin, respectively, and one based on the variances of the differences between the phenotypic values of index and cousin and of index and sib) and the three conventional estimators (one based on regression of index value on midparental value, another based on the sib-sib correlation, and the last one based on the index-cousin correlation) are also made under two genetic models.

Analysis of Variance↗
PubMed1984

Genetic covariation between neuroticism and the symptoms of anxiety and depression.

A genetic analysis of the trait of neuroticism and symptoms of anxiety and depression in 3,810 pairs of adult MZ and DZ twins is reported. Differences between people in these measures can be explained simply by differences in their genes and in their individual environmental experiences. There is no evidence that environmental experiences that are shared by cotwins, such as common family environment or social influences, are important. There are differences between the sexes in gene action affecting neuroticism, and genetic effects become more pronounced with age in females. The lack of evidence for dominance variance affecting neuroticism contrasts well with the detection of considerable genetical nonadditivity for extraversion in the same sample and reinforces the view that these two traits are not only statistically, but also genetically, quite independent. An analysis of the causes of covariation between anxiety, depression, and neuroticism shows that additive gene effects are more important causes of covariation than environmental factors. Genetic variation in symptoms of anxiety and depression is largely dependent on the same factors as effect the neuroticism trait. However, there is also evidence for genetic variation specific to depression.

Adolescent↗
PubMed2001

Replication validity of genetic association studies.

The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

Bias↗
PubMed2001

A genetic analysis of hydrologically dispersed seeds of Hibiscus moscheutos (Malvaceae).

The dispersal of floating seeds in wetland habitats should influence the genetic characteristics of plant metapopulations. We examined gene flow of a hydrochorous wetland macrophyte, Hibiscus moscheutos L. (Malvaceae), by analyzing allozyme variation in current-year floating-seed populations. The genetic composition of floating seeds was compared to the genetic composition of established populations of H. moscheutos that had been previously analyzed in the same areas. The F statistics demonstrated that genetic structuring among floating-seed populations was weak or absent, indicating that seeds from source populations were thoroughly mixed. Floating-seed populations had an excess of homozygotes, a different situation than had previously been found in established populations. The exchange of seeds was greatest among H. moscheutos populations that were adjacent to a tidal stream. We conclude that populations adjacent to the tidal streams are part of a metapopulation that serves as a reserve of genetic variation in the system. Although established populations of H. moscheutos that are not close to the tidal stream are relatively isolated genetically, we found evidence that they also contribute to the floating-seed populations within the estuary.

Journal Article↗
PubMed2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Saccharum↗
PubMed2002

[Comparison of Chuvashs with Maris and Russians by vital statistics and the Crow index].

Genetic demographic characteristics were calculated for Chuvash and Russian inhabitants of the Republic of Chuvashia. The generation lengths were 27.09 and 26.4 years and the sibship sizes were 2.54 and 1.82 for Chuvashes and Russians, respectively. Crow's indices and their components were as follows: Im = 0.05, If = 0.31, and Itot = 0.37 for Chuvashes and Im = 0.03, If = 0.43, and Itot = 0.46 for Russians. The genetic demographic characteristics obtained were compared with those for Highland and Meadow Maris.

Demography↗
PubMed1997

Biased mutations and microsatellite variation.

Mutation bias is one of the forces that may constrain the variation at microsatellite loci. Here, we study the dynamics of population statistics and the genetic distance between two populations under multiple stepwise mutations with linear bias and random drift. Expressions are derived for these statistics as functions of time, as well as at mutation-drift equilibrium. Applying these expressions to published data on humans and chimpanzees, the regression coefficient of mutation bias on allele size was estimated to be at least between -0.0064 and -0.013. The assumption of mutational bias produces larger estimates of divergence times than are obtained in its absence; in particular, the time of split between African and non-African human populations is estimated to be between 183,000 and 222,000 years, assuming one-step mutations and no selection. With multistep mutations, the divergence time is estimated to be lower.

Animals↗
PubMed2000

The robust statistical bases of the coevolution theory of genetic code origin.

A paper (Amirnovin R, J Mol Evol 44:473-476, 1997) seems to undermine the validity of the coevolution theory of genetic code origin by shedding doubt on the connection between the biosynthetic relationships between amino acids and the organization of the genetic code, at a time when the literature on the topic takes this for granted. However, as a few papers cite this paper as evidence against the coevolution theory, and to cast aside all doubt on the subject, we have decided to reanalyze the statistical bases on which this theory is founded. We come to the following conclusions: (1) the methods used in the above referred paper contain certain mistakes, and (2) the statistical foundations on which the coevolution theory is based are extremely robust. We have done this by critically appraising Amirnovin's paper and suggesting an alternative method based on the generation of random codes which, along with the method reported in the literature, allows us to evaluate the significance, in the genetic code, of different sets of amino acid pairs in biosynthetic relationships. In particular, by using this method and after building up a certain set of amino acid pairs reflecting the expectations of the coevolution theory, we show that the presence of this set in the genetic code would be obtained, purely by chance, with a probability of 6x10(-5). This observation seems to provide particularly strong support to the coevolution theory.

Amino Acids↗
PubMed2006

Statistical epistasis is a generic feature of gene regulatory networks.

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.

Epistasis, Genetic↗