Human behavioural genetics of cognitive abilities and disabilities

Although neither the genome nor the environment can be manipulated in research on human behaviour, some of the new tools of molecular genetics can be brought to bear on human behavioural disorders (e.g. cognitive disabilities) and quantitative traits (e.g. cognitive abilities). The inability to manipulate the human genome experimentally has had the positive effect of focusing attention on naturally occuring genetic variation responsible for behavioural differences among individuals in all their complex multifactorial splendour. Genes in such complex multiple-gene systems are called quantitative trait loci (QTLs), which merge the two worlds of genetic research, quantitative genetics and molecular genetics. Although most genetic research on complex human behaviour has focused on severe mental disorders, cognitive abilities and disabilities may be even more immediately relevant to neuroscience. For example, verbal ability and spatial ability are two of the most heritable cognitive abilities, and reading disability is the first behavioural disability for which replicated QTL linkage has been found. The purpose of this essay is to provide an overview of the genetics of cognitive abilities and disabilities as an example of the impending merger of quantitative genetics and molecular genetics in QTL analysis of complex traits.     

作物数量性状研究进展

作物主要农艺性状和经济性状大多属于数量性状。传统数量遗传学采用数理统计方法,把控制数量性状的多基因系统作为一个整体进行研究。DNA分子标记技术的出现和发展,为数量性状研究提供了重要工具。自20世纪80年代以来,QTL定位的统计分析方法发展很快,先后提出单标记分析法、区间作图法及复合区间作图法等。目前,作物QTL研究取得了重要进展,一些重要作物、重要农艺性状的主效QTL基因已被相继克隆成功,作物数量性状的研究已经成为一个具有勃勃生机的热门领域。     

Accounting for variability in the use of permutation testing to detect quantitative trait loci

Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.     

作物数量性状基因座定位及分析研究进展

近年来,分子数量遗传学的快速发展,使作物复杂数量性状尤其是经济性状的QTL研究取得了巨大进展,大大促进了复杂数量性状的遗传改良和分子操纵。本文从分子标记连锁图谱的构建、QTL定位力方法及效应分析、精细定位和QTL验证及应用等方面综述了二十多年来作物QTL的研究进展,讨论当前QTL研究中存在的问题,并展望了作物QTL研究的发展前景。     

Genome size, quantitative genetics and the genomic basis for flower size evolution in Silene latifolia

BACKGROUND AND AIMS: The overall goal of this paper is to construct an overview of the genetic basis for flower size evolution in Silene latifolia. It aims to examine the relationship between the molecular bases for flower size and the underlying assumption of quantitative genetics theory that quantitative variation is ultimately due to the impact of a number of structural genes. SCOPE: Previous work is reviewed on the quantitative genetics and potential for response to selection on flower size, and the relationship between flower size and nuclear DNA content in S. latifolia. These earlier findings provide a framework within which to consider more recent analyses of a joint quantitative trait loci (QTL) analysis of flower size and DNA content in this species. KEY RESULTS: Flower size is a character that fits the classical quantitative genetics model of inheritance very nicely. However, an earlier finding that flower size is correlated with nuclear DNA content suggested that quantitative aspects of genome composition rather than allelic substitution at structural loci might play a major role in the evolution of flower size. The present results reported here show that QTL for flower size are correlated with QTL for DNA content, further corroborating an earlier result and providing additional support for the conclusion that localized variations in DNA content underlie evolutionary changes in flower size. CONCLUSIONS: The search image for QTL should be broadened to include overall aspects of genome regulation. As we prepare to enter the much-heralded post-genomic era, we also need to revisit our overall models of the relationship between genotype and phenotype to encompass aspects of genome structure and composition beyond structural genes.     

From plant genomics to breeding practice

New alleles are constantly accumulated during intentional crop selection. The molecular understanding of these alleles has stimulated new genomic approaches to mapping quantitative trait loci (QTL) and haplotype multiplicity of the genes concerned. A limited number of quantitative trait nucleotides responsible for QTL variation have been described, but an acceleration in their rate of discovery is expected with the adoption of linkage disequilibrium and candidate gene strategies for QTL fine mapping and cloning. Additional layers of regulatory variation have been studied that could also contribute to the molecular basis of quantitative genetics of crop traits. Despite this progress, the role of marker-assisted selection in plant breeding will ultimately depend on the genetic model underlying quantitative variation.     

数量性状由表型变异到基因发现的研究进展

分子生物学的快速发展为研究数量性状的遗传基础提供了更为有效的途径。我们可以沿着由表型变异去发现基因之路, 更准确地剖析数量性状的遗传基础; 尤其是对作物的许多重要的数量性状进行的QTL研究越来越受到重视。文章对数量遗传发展, QTL作图群体和方法的发展, QTL定位和QTG(quantitative traits genes)的鉴别方面的现状进行了综述。     

CONNECTING QTLS TO THE G-MATRIX OF EVOLUTIONARY QUANTITATIVE GENETICS

Evolutionary quantitative genetics has recently advanced in two distinct streams. Many biologists address evolutionary questions by estimating phenotypic selection and genetic (co)variances ( G matrices). Simultaneously, an increasing number of studies have applied quantitative trait locus (QTL) mapping methods to dissect variation. Both conceptual and practical difficulties have isolated these two foci of quantitative genetics. A conceptual integration follows from the recognition that QTL allele frequencies are the essential variables relating the G -matrix to marker-based mapping experiments. Breeding designs initiated from randomly selected parental genotypes can be used to estimate QTL-specific genetic (co)variances. These statistics appropriately distill allelic variation and provide an explicit population context for QTL mapping estimates. Within this framework, one can parse the G -matrix into a set of mutually exclusive genomic components and ask whether these parts are similar or dissimilar in their respective features, for example the magnitude of phenotypic effects and the extent and nature of pleiotropy. As these features are critical determinants of sustained response to selection, the integration of QTL mapping methods into G -matrix estimation can provide a concrete, genetically based experimental program to investigate the evolutionary potential of natural populations.     

Germplasm-regression-combined (GRC) marker-trait association identification in plant breeding: a challenge for plant biotechnological breeding under soil water deficit conditions

In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL) identification and marker assisted selection (MAS). However, results have been modest. This has been due to several factors including absence of tight linkage QTL, non-availability of mapping populations, and substantial time needed to develop such populations. To overcome these limitations, and as an alternative to planned populations, molecular marker–trait associations have been identified by the combination between germplasm and the regression technique. In the present preview, the authors (1) survey the successful applications of germplasm–regression–combined (GRC) molecular marker–trait association identification in plants; (2) describe how to do the GRC analysis and its differences from mapping QTL based on a linkage map reconstructed from the planned populations; (3) consider the factors that affect the GRC association identification, including selections of optimal germplasm and molecular markers and testing of identification efficiency of markers associated with traits; and (4) finally discuss the future prospects of GRC marker–trait association analysis used in plant MAS/QTL breeding programs, especially in long-juvenile woody plants when no other genetic information such as linkage maps and QTL are available.     

Family-based mapping of quantitative trait loci in plant breeding populations with resistance to Fusarium head blight in wheat as an illustration

Traditional quantitative trait loci (QTL) mapping approaches are typically based on early or advanced generation analysis of bi-parental populations. A limitation associated with this methodology is the fact that mapping populations rarely give rise to new cultivars. Additionally, markers linked to the QTL of interest are often not immediately available for use in breeding and they may not be useful within diverse genetic backgrounds. Use of breeding populations for simultaneous QTL mapping, marker validation, marker assisted selection (MAS), and cultivar release has recently caught the attention of plant breeders to circumvent the weaknesses of conventional QTL mapping. The first objective of this study was to test the feasibility of using family-pedigree based QTL mapping techniques generally used with humans and animals within plant breeding populations (PBPs). The second objective was to evaluate two methods (linkage and association) to detect marker-QTL associations. The techniques described in this study were applied to map the well characterized QTL, Fhb1 for Fusarium head blight resistance in wheat (Triticum aestivum L.). The experimental populations consisted of 82 families and 793 individuals. The QTL was mapped using both linkage (variance component and pedigree-wide regression) and association (using quantitative transmission disequilibrium test, QTDT) approaches developed for extended family-pedigrees. Each approach successfully identified the known QTL location with a high probability value. Markers linked to the QTL explained 40–50% of the phenotypic variation. These results show the usefulness of a human genetics approach to detect QTL in PBPs and subsequent use in MAS.     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

番茄果重数量性状基因的研究进展及在遗传学教学中的应用

遗传学发展史上一系列经典的研究案例对学科的发展起了巨大的推动作用,将这些经典案例与教学内容相结合应用到遗传学课程教学中,对学生的科学思维和遗传分析能力是一个很好的训练。番茄果重基因的定位与克隆在数量性状基因座研究中是开创性的工作,完整的体现了植物数量性状基因的研究历程。将其作为一个综合案例应用于遗传学教学,可以生动直观地给学生展示一个精彩的科学发现过程,展现遗传学研究的魅力,激发学生的学习兴趣,收到了很好的教学效果。     

Multiple QTL mapping in related plant populations via a pedigree-analysis approach

QTL mapping experiments in plant breeding may involve multiple populations or pedigrees that are related through their ancestors. These known relationships have often been ignored for the sake of statistical analysis, despite their potential increase in power of mapping. We describe here a Bayesian method for QTL mapping in complex plant populations and reported the results from its application to a (previously analysed) potato data set. This Bayesian method was originally developed for human genetics data, and we have proved that it is useful for complex plant populations as well, based on a sensitivity analysis that was performed here. The method accommodates robustness to complex structures in pedigree data, full flexibility in the estimation of the number of QTL across multiple chromosomes, thereby accounting for uncertainties in the transmission of QTL and marker alleles due to incomplete marker information, and the simultaneous inclusion of non-genetic factors affecting the quantitative trait.     

Estimating the genetic architecture of quantitative traits

Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.     

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.     

Genetics of species differences in the wild annual sunflowers, Helianthus annuus and H. petiolaris

Much of our knowledge of speciation genetics stems from quantitative trait locus (QTL) studies. However, interpretations of the size and distribution of QTL underlying species differences are complicated by differences in the way QTL magnitudes are estimated. Also, many studies fail to exploit information about QTL directions or to compare inter- and intraspecific QTL variation. Here, we comprehensively analyze an extensive QTL data set for an interspecific backcross between two wild annual sunflowers, Helianthus annuus and H. petiolaris, interpret different estimates of QTL magnitudes, identify trait groups that have diverged through selection, and compare inter- and intraspecific QTL magnitudes. Our results indicate that even minor QTL (in terms of backcross variance) may be surprisingly large compared to levels of standing variation in the parental species or phenotypic differences between them. Morphological traits, particularly flower morphology, were more strongly or consistently selected than life history or physiological traits. Also, intraspecific QTL were generally smaller than interspecific ones, consistent with the prediction that larger QTL are more likely to spread to fixation across a subdivided population. Our results inform the genetics of species differences in Helianthus and suggest an approach for the simultaneous mapping of inter- and intraspecific QTL.     

Quantitative trait loci analysis of primary cell wall composition in Arabidopsis

Quantitative trait loci (QTL) analysis was used to identify genes underlying natural variation in primary cell wall composition in Arabidopsis (Arabidopsis thaliana). The cell walls of dark-grown seedlings of a Bay-0 x Shahdara recombinant inbred line population were analyzed using three miniaturized global cell wall fingerprinting techniques: monosaccharide composition analysis by gas chromatography, xyloglucan oligosaccharide mass profiling, and whole-wall Fourier-transform infrared microspectroscopy. Heritable variation and transgression were observed for the arabinose-rhamnose ratio, xyloglucan side-chain composition (including O-acetylation levels), and absorbance for a subset of Fourier-transform infrared wavenumbers. In total, 33 QTL, corresponding to at least 11 different loci controlling dark-grown hypocotyl length, pectin composition, and levels of xyloglucan fucosylation and O-acetylation, were identified. One major QTL, accounting for 51% of the variation in the arabinose-rhamnose ratio, affected the number of arabinan side chains presumably attached to the pectic polysaccharide rhamnogalacturonan I, paving the way to positional cloning of the first gene underlying natural variation in pectin structure. Several QTL were found to be colocalized, which may have implications for the regulation of xyloglucan metabolism. These results demonstrate the feasibility of combining fingerprinting techniques, natural variation, and quantitative genetics to gain original insight into the molecular mechanisms underlying the structure and metabolism of cell wall polysaccharides.     

Quantitative trait locus analysis of longitudinal quantitative trait data in complex pedigrees

There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.     

Advancing Genetic Theory and Application by Metabolic Quantitative Trait Loci Analysis

This review describes recent advances in the analysis of metabolism using quantitative genetics. It focuses on how recent metabolic quantitative trait loci (QTL) studies enhance our understanding of the genetic architecture underlying naturally variable phenotypes and the impact of this fundamental research on agriculture, specifically crop breeding. In particular, the role of whole-genome duplications in generating quantitative genetic variation within a species is highlighted and the potential uses of this phenomenon presented. Additionally, the review describes how new observations from metabolic QTL mapping analyses are helping to shape and expand the concepts of genetic epistasis.