固氮树种在混交林中的作用研究Ⅲ.固氮树种凋落物分解及N的释放

研究了１１种固氮和非固氮树种凋落物分解及可利用态Ｎ的释放．结果表明,固氮树木凋落物中Ｎ的生物分解系数、落叶分解过程中干重的损失率及有效态氮的释放分别比非固氮树种高出１．３、１．５和８．２倍．     

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects

Background

The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student''s-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student''s-t (BSt) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models.

Methods

In the simulation study, bivariate residuals were generated using Student''s-t distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student''s-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student''s-t distributions with unknown degrees of freedom.

Results

Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student''s-t error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student''s-t model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models.

Conclusions

Reliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.     

Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.     

FaST linear mixed models for genome-wide association studies

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).     

Score test for linkage in generalized linear models

We derive a test for linkage in a Generalized Linear Mixed Model (GLMM) framework which provides a natural adjustment for marginal covariate effects. The method boils down to the score test of a quasi-likelihood derived from the GLMM, it is computationally inexpensive and can be applied to arbitrary pedigrees. In particular, for binary traits, relative pairs of different nature (affected and discordant) and individuals with different covariate values can be naturally combined in a single test. The model introduced could explain a number of situations usually described as gene by covariate interaction phenomena, and offers substantial gains in efficiency compared to methods classically used in those instances.     

The use of linear mixed model theory for the genetic analysis of repeated measures from clonal tests of forest trees. I. A focus on spatially repeated data

The paper reviews the linear mixed models (LMM) methodology that is suitable for the statistical and genetic analyses of spatially repeated measures collected from clonal progeny tests. For example, we consider a poplar clonal trial where progenies of different families are propagated by cuttings, and only one ramet per clone is planted on each block. Modeling covariance structures following the LMM theory allows improving genetic parameter estimation based on clonal testing. Besides variance components, we also obtained an estimate of the covariance between residuals (within clonal effects in two different blocks). This covariance is due to planting more than one ramet from the same genotype in the same trial, which generates correlated residual effects from different blocks. Its estimation can significantly improve the comparison among clones within a progeny test or between tests in a clonal testing network. Results indicate that the covariance is also a component of the genetic variance estimator and plays a significant role in assessing the variance of specific (micro) environmental effects. A positive covariance implies that ramets show a similar performance in more than one block. Thus, a larger and more positive covariance implies a stronger genetic effect controlling the expression of the trait in the local environment and a smaller variance of specific environmental effects. On the contrary, a negative covariance implies that the performance of individual ramets is affected by strong microenvironmental effects, specific to one or more blocks, which can also directly increase the within-clone variability.     

Across-species patterns of genetic variation in forest trees of Central Europe

The study focuses on geographical patterns of genetic variation at allozyme loci common for four main tree species of Central Europe (Norway spruce, silver fir, common beech and sessile oak). Moving-window averaging of four indicators of allelic richness and diversity (proportion of polymorphic loci, mean number of alleles per locus, effective number of alleles and expected heterozygosity) with window size of 50 × 50 km was used to identify the patterns. Moreover, local genetic divergence was assessed using the G _ST (Nei, Molecular population genetic and evolution, Amsterdam and Oxford, North-Holland, 1975) and D _j (Gregorius and Roberds, Theor Appl Genet 71:826–834, 1986) statistics for common beech and silver fir, where raw genotype data were available. Spatial patterns of diversity and allelic richness were quite similar. Romanian Carpathians were identified as the most important hotspot of genetic diversity and evolutionary divergence in Central Europe. Implications for genetic conservation are briefly discussed.     

Generalized linear mixed models for mapping multiple quantitative trait loci

Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.     

Population dynamics of coniferous and broad-leaved trees in a Japanese temperate mixed forest

The growth and survival of coniferous and broad-leaved trees were followed over a 5-yr period in a temperate old-growth mixed forest in Japan, and dynamic features of the forest were studied in relation to the life history of the dominants, the coniferous Abies homolepis and the broad-leaved Fagus crenata. During this period, the gap formation rate was 31m² ha^?1yr^?1, the mortality of trees > 2m high was 1.7%/yr, and the rate of loss in basal area 1.4%/yr. These values were much higher than the recruitment, 0.3%/yr, and the total growth of surviving and new trees, 0.6%/yr, owing to the inhibition of regeneration by understorey dwarf bamboo (Sasa borealis). A transition matrix model based on DBH size classes predicts that the basal area of the forest will decrease by 14% in 50 yr, but that the DBH distribution of trees > 10 cm diameter will change little. Equilibrium DBH distributions assuming recruitment being equal to mortality, were quite different between broad-leaved and coniferous trees, reflecting different survivorship curves of the two dominants. The composition and structure of the forest may change depending on the pattern and frequency of disturbances, or episodic events, notably the synchronous death of Sasa borealis.     

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix RSs were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.