Evolution of balanced genetic polymorphism

Extreme genetic polymorphism maintained by balancing selection (so called because many alleles are maintained in a balance by a mechanism of rare allele advantage) is intimately associated with the important task of self/non-self-discrimination. Widely disparate self-recognition systems of plants, animals and fungi share several general features, including the maintenance of large numbers of alleles at relatively even frequency, and persistence of this variation over very long time periods. Because the evolutionary dynamics of balanced polymorphism are very different from those of neutral genetic variation, data on balanced polymorphism have been used as a novel source for inference of the history of populations. This review highlights the unique evolutionary properties of balanced genetic polymorphism, and the use of theoretical understanding in analysis and application of empirical data for inference of population history. However, a second goal of this review is to point out where current theory is incomplete. Recent observations suggest that entirely novel selective forces may act in concert with balancing selection, and these novel forces may be extremely potent in shaping genetic variation at self-recognition loci.     

Integrating patterns of polymorphism at SNPs and STRs

Single nucleotide polymorphisms (SNPs) and short tandem repeats (STRs) differ in mutation rate and mechanism. As a result of these differences, simultaneous consideration of polymorphism patterns at SNPs and STRs can provide insights that are difficult to obtain from analysis of either marker type in isolation. Here, we use coalescent simulations to model the opposing effects of contrasting mutational dynamics and of shared genealogical history on the correlation between polymorphism at linked SNPs and STRs. Results show that polymorphism patterns are correlated only weakly despite the shared underlying genealogy, underscoring the importance of divergent mutational processes. Examples illustrate how knowledge of these relationships could aid population genetic inference, indicating the need for thorough theoretical studies.     

Molecular phylogeny of extant equids and effects of ancestral polymorphism in resolving species-level phylogenies

Short divergence times and processes such as incomplete lineage sorting and species hybridization are known to hinder the inference of species-level phylogenies due to the lack of sufficient informative genetic variation or the presence of shared but incongruent polymorphism among taxa. Extant equids (horses, zebras, and asses) are an example of a recently evolved group of mammals with an unresolved phylogeny, despite a large number of molecular studies. Previous surveys have proposed trees with rather poorly supported nodes, and the bias caused by genetic introgression or ancestral polymorphism has not been assessed. Here we studied the phylogenetic relationships of all extant species of Equidae by analyzing 22 partial mitochondrial and nuclear genes using maximum likelihood and Bayesian inferences that account for heterogeneous gene histories. We also examined genetic signatures of lineage sorting and/or genetic introgression in zebras by evaluating patterns of intraspecific genetic variation. Our study improved the resolution and support of the Equus phylogeny and in particular the controversial positions of the African wild ass (E. asinus) and mountain zebra (E. zebra): the African wild ass is placed as a sister species of the Asiatic asses and the mountain zebra as the sister taxon of Grevy's and Burchell's zebras. A shared polymorphism (indel) detected among zebra species in the Estrogen receptor 1 gene was likely due to incomplete lineage sorting and not genetic introgression as also indicated by other mitochondrial (Cytochrome b) and nuclear (Y chromosome and microsatellites) markers. Ancestral polymorphism in equids might have contributed to the long-standing lack of clarity in the phylogeny of this highly threatened group of mammals.     

The joint allele frequency spectrum of multiple populations: a coalescent theory approach

The allele frequency spectrum is a series of statistics that describe genetic polymorphism, and is commonly used for inferring population genetic parameters and detecting natural selection. Population genetic theory on the allele frequency spectrum for a single population has been well studied using both coalescent theory and diffusion equations. Recently, the theory was extended to the joint allele frequency spectrum (JAFS) for three populations using diffusion equations and was shown to be very useful in inferring human demographic history. In this paper, I show that the JAFS can be analytically derived with coalescent theory for a basic model of two isolated populations and then extended to multiple populations and various complex scenarios, such as those involving population growth and bottleneck, migration, and positive selection. Simulation study is used to demonstrate the accuracy and applicability of the theoretical model. The coalescent theory-based approach for the JAFS can characterize the demographic history with comprehensive statistical models as the diffusion approach does, and in addition gains several novel advantages: the computational complexity of calculating the JAFS with coalescent theory is reduced, and thus it is feasible to analytically obtain the JAFS for multiple populations; the hitchhiking effect can be efficiently modeled in coalescent theory, enabling the development of methodologies for detecting selection via multi-population polymorphism data. As an alternative to the diffusion approximation approach, the coalescent theory for the JAFS also provides a foundation for population genetic inference with the advent of large-scale genomic polymorphism data.     

Bayesian approach to searching for susceptibility genes: Gc2 and EsD1 alleles and multiple sclerosis

Multiple sclerosis (MS) is one of the most common causes of neurological disability in early adulthood. The current literature is interested in identifying biological or DNA markers associated with genetic susceptibility to MS. The aim of this study is to investigate, by means of Bayesian statistical inference, whether the presence of Gc2 (Gc = group-specific component) and/or EsD1 (EsD = esterase D) alleles affects MS susceptibility. Gc and EsD are two classical genetic markers, being the first a serum protein polymorphism, the latter an isoenzyme polymorphism. The interest of the proposed statistical approach of searching for MS susceptibility genes relies on the analysis of two different functions, one function being inferred from our results on 56 unrelated patients from central Italy affected by MS, the other one from Italian and worldwide epidemiological data. The graphical analysis suggests that MS susceptibility is influenced by both Gc2 and EsD1 alleles; and EsD1 allele is more informative than Gc2. These results point out the advantages of the Bayesian approach in searching for susceptibility genes. Furthermore, the significant association between the considered alleles and the susceptibility to MS suggests possible hypotheses about the pathogenesis of the disease.     

ISSR Analysis of Variability of Cultivated Form and Varieties of Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L.) from Azerbaijan

The article presents the results of a study of genetic polymorphism for the first time carried out on pomegranate varieties and forms of Azerbaijan origin using molecular markers. In total, 102 PCR fragments were identified, of which 80 were polymorphic. The high level of polymorphism (75.5%) and the rich genetic diversity were identified among the studied pomegranate collection. As a result of data analysis and on the basis of the values of the basic parameters (PIC, EMR, MI, RP, MRP) determining informativeness of markers, all 14 ISSR primers were suitable for genotyping pomegranate accessions. The most effective markers (UBC808, UBC811, UBC834, and UBC840) were identified among the set of primers tested. A dendrogram was constructed on the basis of the data obtained, which made it possible to group genotypes into 16 major clusters. The genetic similarity index ranged from 0.032 to 0.94. The study of the genetic relationship of different pomegranate varieties confirms the effectiveness of the ISSR method, which makes it possible to determine the level of genetic diversity, as well as to establish the relationship among the studied pomegranate accessions.     

Assessment of genetic and epigenetic variation in hop plants regenerated from sequential subcultures of organogenic calli

Organogenic calli induced from internodal segments were subcultured three times. Regenerated plants obtained from each subculture were analysed by molecular methods. No major genetic rearrangements were detected in the callus-derived plants since none of the amplified fragment-length polymorphism (AFLP) loci were found to be polymorphic. However, epigenetic changes due to a demethylation process were detected by methylation-sensitive amplified polymorphism (MSAP) technique. The results allowed inference of the possible relationship among the plants derived from different calli subcultures and the in vitro control. The plants recovered from the first and second callus subcultures clustered with the in vitro control pools in the phenogram while the regenerants from the third callus subculture showed the highest genetic distance with the controls. This is the first study reporting data about the genetic stability of callus-derived Humulus lupulus L. plants.     

Combining evidence for association from transmission disequilibrium and case-control studies using single-nucleotide polymorphisms

The aim of the present analysis is to combine evidence for association from the two most commonly used designs in genetic association analysis, the case-control design and the transmission disequilibrium test (TDT) design. The cases here are affected offspring from nuclear families and are used in both the case-control and TDT designs. As a result, inference from these designs is not independent. We applied a simple logistic regression method for combining evidence for association from case-control and TDT designs to single-nucleotide polymorphism data purchased on a region on chromosome 3, replicate 1 of the Aipotu population. Combining the evidence from the case-control and TDT designs yielded a 5-10% reduction in the standard errors of the relative risk estimates. The authors did not know the results before the analyses were conducted.     

Population divergence with or without admixture: selecting models using an ABC approach

Genetic data have been widely used to reconstruct the demographic history of populations, including the estimation of migration rates, divergence times and relative admixture contribution from different populations. Recently, increasing interest has been given to the ability of genetic data to distinguish alternative models. One of the issues that has plagued this kind of inference is that ancestral shared polymorphism is often difficult to separate from admixture or gene flow. Here, we applied an approximate Bayesian computation (ABC) approach to select the model that best fits microsatellite data among alternative splitting and admixture models. We performed a simulation study and showed that with reasonably large data sets (20 loci) it is possible to identify with a high level of accuracy the model that generated the data. This suggests that it is possible to distinguish genetic patterns due to past admixture events from those due to shared polymorphism (population split without admixture). We then apply this approach to microsatellite data from an endangered and endemic Iberian freshwater fish species, in which a clustering analysis suggested that one of the populations could be admixed. In contrast, our results suggest that the observed genetic patterns are better explained by a population split model without admixture.     

Minor allele frequency thresholds strongly affect population structure inference with genomic data sets

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure—often the first step in analysis of population genomic data. We find that model‐based inference of population structure is confounded when singletons are included in the alignment, and that both model‐based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.     

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability.     

基因多态性在盆底器官脱垂中的研究进展

盆底器官脱垂（Pelvic Organ Prolapse,POP）是遗传和环境因素共同作用引起的中老年妇女常见病,其发病率逐年上升。目前,环境因素已基本明确,而遗传因素尚未阐明。近年来,由于基因分型及遗传基因鉴定技术的快速发展,以及对盆底器官脱垂的群体调查及家系分析的增加,盆腔脏器脱垂的遗传研究有一定的进展,本文就其在基因多态性方面的发现做一综述。     

Evidence for a large-scale population structure among accessions of Arabidopsis thaliana: possible causes and consequences for the distribution of linkage disequilibrium

The existence of a large-scale population structure was investigated in Arabidopsis thaliana by studying patterns of polymorphism in a set of 71 European accessions. We used sequence polymorphism surveyed in 10 fragments of approximately 600 nucleotides and a set of nine microsatellite markers. Population structure was investigated using a model-based inference framework. Among the accessions studied, the presence of four groups was inferred using genetic data, without using prior information on the geographical origin of the accessions. Significant genetic isolation by geographical distance was detected at the group level, together with a geographical gradient in allelic richness across groups. These results are discussed with respect to the previously proposed scenario of postglacial colonization of Europe from putative glacial refugia. Finally, the contribution of the inferred structure to linkage disequilibrium among 171 pairs of essentially unlinked markers was also investigated. Linkage disequilibrium analysis revealed that significant associations detected in the whole sample were mainly due to genetic differentiation among the inferred groups. We discuss the implication of this finding for future association studies in A. thaliana.     

Comparative analysis of genetic structure of 2 species of marine fish Dicentrarchus labrax and Dicentrarchus punctatus

We present here the genetic structure existing among five samples of the spotted sea bass Dicentrarchus punctatus, and we compare it to what prevails in the common sea bass D. labrax, a congeneric species sampled on almost the same geographical range. A genetic distance tree inferred from the polymorphism at six microsatellite loci shows a distinct pattern for the two species. D. labrax samples appears to be genetically more homogeneous with a global Fst of 3% as compared to the 10% observed at D. punctatus, indicating a lesser level of gene flow in the latter species. While appearing more differentiated, D. punctatus presents no clear geographical organisation of its genetic variability in opposition to D. labrax samples. This allows us to propose this pair of closely relative species as a good candidate for the study by comparative analysis of the biological and/or historical factors affecting genetic differentiation in marine environment.     

Identification and characterization of simple sequence repeat (SSR) markers derived in silico from Brassica oleracea genome shotgun sequences

The availability of sequence data derived from shotgun sequencing programs enables mining for simple sequence repeats (SSRs), providing useful genetic markers for crop improvement. This study presents the development and characterization of 40 SSR markers from Brassica oleracea shotgun sequence and their cross‐amplification across Brassica species. The markers show reliable amplification, genome specificity and considerable polymorphism, demonstrating the utility of SSRs for genetic analysis of commercial Brassica germplasm.     

Sixteen new simple sequence repeat markers from Brassica juncea expressed sequences and their cross‐species amplification

The availability of expressed sequence data derived from gene discovery programs enables mining for simple sequence repeats (SSR), providing useful genetic markers for crop improvement. These markers are inexpensive, require minimal labour to produce and can frequently be associated with functionally annotated genes. This study presents the development and characterization of 16 expressed sequence tags (EST)‐SSR markers from Brassica juncea and their cross‐amplification across Brassica species. Sixteen primer pairs were assessed for polymorphism in all genomes of the diploid and amphidiploid Brassica species. The markers show reliable amplification, considerable polymorphism and high transferability across species, demonstrating the utility of EST‐SSRs for genetic analysis of brassicas.     

微藻育种与培养研究概况

微藻类的天然产物具有广泛的遗传变异和潜在的经济价值,但是这些天然产物的获取还依赖于品种的改良和适宜的培养条件。本文概括性地论述了微藻的遗传育种和优化培养。它包括微藻类的遗传多样性、选择育种、诱变育种、细胞融合、基因工程以及固定化培养和生物反应器的应用。这些生物技术为微藻类的利用开辟了广阔的前景。     

SSR分子标记在作物遗传育种中的应用

SSR(simple sequence repeat)是建立在PCR技术上的一种广泛应用的分子标记,具有含量丰富、多态性高、共显性等优点。本文简要介绍了SSR分子标记技术的原理和特点,重点介绍了SSR分子标记技术在作物遗传育种中的应用,主要在作物遗传多样性、基因定位、分子辅助标记、遗传图谱构建、品种鉴定和纯度鉴定等方面进行阐述。     

Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits

Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.