A new statistical method for mapping QTLs underlying endosperm traits

Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci （QTLs） underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs controlling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to endosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the precision of QTL mapping. The simulation results show the proposed method provides accurate estimates of the QTL effects and locations with high statistical power.     

Genome-Wide Interaction-Based Association of Human Diseases—A Survey

Genome-Wide Association Studies（GWASs） aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms（SNPs）. Although traditional single-locus statistical approaches have been standardized and led to many interesting findings, a substantial number of recent GWASs indicate that for most disorders, the individual SNPs explain only a small fraction of the genetic causes. Consequently, exploring multi-SNPs interactions in the hope of discovering more significant associations has attracted more attentions. Due to the huge search space for complicated multilocus interactions, many fast and effective methods have recently been proposed for detecting disease-associated epistatic interactions using GWAS data. In this paper, we provide a critical review and comparison of eight popular methods, i.e., BOOST, TEAM, epi Forest, EDCF, SNPHarvester, epi MODE, MECPM, and MIC, which are used for detecting gene-gene interactions among genetic loci. In views of the assumption model on the data and searching strategies, we divide the methods into seven categories. Moreover, the evaluation methodologies,including detecting powers, disease models for simulation, resources of real GWAS data, and the control of false discover rate, are elaborated as references for new approach developers. At the end of the paper, we summarize the methods and discuss the future directions in genome-wide association studies for detecting epistatic interactions.     

Advances on methods for mapping QTL in plant

Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.     

Statistical approaches in QTL mapping and molecular breeding for complex traits

Most of the important agronomic traits in crops,such as yield and quality,are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction.Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs.Conventionally,the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects.With recent advances in molecular genotyping and high-throughput technology,the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible.The improvement of complex traits has also been achieved by pyramiding individual QTL.In this review,we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction,and discuss their applications in crop breeding for complex traits.     

Estimation of statistical power and false discovery rate of QTL mapping methods through computer simulation

Many QTL mapping methods have been developed in the past two decades.Statistically,the best method should have a high detection power but a low false discovery rate (FDR).Power and FDR cannot be derived theoretically for most QTL mapping methods,but they can be properly evaluated using computer simulations.In this paper,we used four genetic models (two for independent loci and two for linked loci) to illustrate power and FDR estimation for interval mapping (IM) and inclusive composite interval mapping (ICIM).For each model,we simulated 1000 populations each of 200 doubled haploids.A support interval (SI) was first defined to indicate to which predefined QTL the significant QTL belonged.Power was calculated by counting the number of simulation runs with significant peaks higher than the logarithm of odds (LOD) threshold in the SI.Quantitative trait loci not identified in any SIs were viewed as false positives.The FDR is the rate at which QTLs are identified as significant when they are actually non-significant.Simulation results allowed us to estimate power and FDR of IM and ICIM for two independent and two linkage genetic models.Our estimates allowed us to readily compare the efficiencies of different statistical methods for QTL mapping,including the ability to separate linkage,under a wide range of genetic models.We used IM and ICIM as examples of how to estimate power and FDR,but the principles shown in this paper can be used for power analysis and comparison of any other QTL mapping methods,especially those based on interval tests.     

Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

Chromosome segment substitution lines have been created in several experimental models,including many plant and animal species,and are useful tools for the genetic analysis and mapping of complex traits.The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study.Furthermore,we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects,examined the fully Bayesian SSVS (stochastic search variable selection) approach,tested the empirical Bayes (E-BAYES) method,and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.     

Dissecting the architecture of a quantitative trait locus in yeast

Most phenotypic diversity in natural populations is characterized by differences in degree rather than in kind. Identification of the actual genes underlying these quantitative traits has proved difficult. As a result, little is known about their genetic architecture. The failures are thought to be due to the different contributions of many underlying genes to the phenotype and the ability of different combinations of genes and environmental factors to produce similar phenotypes. This study combined genome-wide mapping and a new genetic technique named reciprocal-hemizygosity analysis to achieve the complete dissection of a quantitative trait locus (QTL) in Saccharomyces cerevisiae. A QTL architecture was uncovered that was more complex than expected. Functional linkages both in cis and in trans were found between three tightly linked quantitative trait genes that are neither necessary nor sufficient in isolation. This arrangement of alleles explains heterosis (hybrid vigour), the increased fitness of the heterozygote compared with homozygotes. It also demonstrates a deficiency in current approaches to QTL dissection with implications extending to traits in other organisms, including human genetic diseases.     

复合性状的QTL定位模型构建

【目的】传统复合性状的QTL(quantitative trait locus)定位方法仅仅利用两个或几个构成性状的计算值作为表型值,未考虑复合性状的生物学内涵,从而影响定位的准确性。因此,发展适合于复合性状的QTL定位模型,对于深入解析控制复合性状的遗传结构,进而提高基因定位准确性显得越来越重要。【方法】针对全基因组重测序数据,构建了一个复合性状QTL定位模型(composite traits mapping model, CTM),利用CTM对复合性状进行分解,把分解后的组分以二元或多元正态分布形式整合到QTL作图的框架内。【结果】应用CTM分析杨树材积生长数据,可成功定位到大量与杨树材积生长相关的基因,并与传统方法进行了比较,定位出较多的显著位点,表现出较好的性能。计算机模拟试验表明,所构建的CTM模型在定位复合性状QTL中具有较高的效力,在达到一定的样本数量和遗传力条件下,CTM模型具有较强的效力,样本量和遗传力的增加都能够增加参数估计的精度。【结论】CTM模型有助于复合性状遗传结构的解析,促进林木分子标记辅助育种的开展。     

Quantitative genetic analysis station for the genetic analysis of complex traits

The Quantitative Genetic Analysis Station (QGAStation) is a software package that has been developed to perform statistical analysis for complex traits.It consists of five domains for handling data from diallel crosses,regional trials,core germplasm collections,QTL mapping,and microarray experiments.The first domain contains genetic models for diallel cross analysis,in which genetic variance components and genetic-by-environment interactions can be estimated,and genetic effects can be predicted.The second domain evaluates the performance of varieties in regional trials by implementing a general statistical method that outperforms ANOVA in tackling unbalanced data that arises frequently in trials across multiple locations and over a number of years.The third domain,using predicted genotypic values as proxy,constructs core germplasm collections covering sufficient genetic diversity with lower redundancy.The fourth domain manages genotypic and phenotypic data for QTL mapping.Linkage maps can be constructed and genetic distances can be estimated;the statistical methods that have been implemented apply to both chiasmatic and achiasmatic organisms.Another part of this domain can filter systematic noises in phenotypic data.The fifth domain focuses on the cDNA expression data that is generated by microarray experiments.A two-step strategy has been implemented to detect differentially expressed genes and to estimate their effects.Except in the fourth domain,the major statistical methods that have been used are mixed linear model approaches that have been implemented in the C language.Computational efficiency is further boosted for computers that are equipped with graphics processing units (GPUs).A user friendly graphic interface is provided for Microsoft Windows and Apple Mac operating systems.QGAStation is available at http://ibi.zju.edu.cn/software/qga/.     

动态复杂性状遗传结构研究的统计模型

许多重要的农艺性状、生物学和生物医学性状都是数量性状,这些性状在不同的发育阶段发生变化,并表现出复杂的动态特征。针对这些动态性状,传统的遗传作图方法是通过在不同的年龄或发育阶段利用遗传标记与表型性状进行关联分析,并比较这些性状在不同发育阶段的差异,或者通过进行不同阶段的多位点作图进行分析。然而,这些方法并不能确切地反映整个发育过程和动态特点,这使得对性状遗传结构的推测受到限制。要克服这一困难,函数作图将为动态性状的遗传学研究提供一条有效的途径。函数作图综合了生物学机制的数学特性和性状遗传作图的统计学特点,结合统计模型、遗传学和发育生物学的函数作图策略,力求解决诸如发育的遗传控制模式、QTL的持续效应以及引起发育过程中启动和终止的遗传机制等问题。笔者提出的函数作图策略将提供一个研究基因作用及互作与发育模式之间有效的量化检测平台。     

果蝇分子群体遗传研究分析方法概述

果蝇属物种长期以来被作为基础生物学、特别是群体和进化遗传学的模式物种,来探讨生物学中的一些基本问题,如物种形成的遗传机制、新基因的起源及其方式、适应性进化的遗传机制等.群体遗传的方法贯穿于遗传学、生态学、古生物学、系统发育学等领域之中,已渐成为基础生物学研究的重要方法.围绕果蝇群体遗传研究中的3个主要问题,即起源地、群体历史动态和群体遗传结构,就当前用于分析上述问题的方法进行了概述,并强调了发展适用于多个座位数据的分析方法是将来的一个重要发展方向.     

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P?相似文献   

Epistasis and balanced polymorphism influencing complex trait variation

Complex traits such as human disease, growth rate, or crop yield are polygenic, or determined by the contributions from numerous genes in a quantitative manner. Although progress has been made in identifying major quantitative trait loci (QTL), experimental constraints have limited our knowledge of small-effect QTL, which may be responsible for a large proportion of trait variation. Here, we identified and dissected a one-centimorgan chromosome interval in Arabidopsis thaliana without regard to its effect on growth rate, and examined the signature of historical sequence polymorphism among Arabidopsis accessions. We found that the interval contained two growth rate QTL within 210 kilobases. Both QTL showed epistasis; that is, their phenotypic effects depended on the genetic background. This amount of complexity in such a small area suggests a highly polygenic architecture of quantitative variation, much more than previously documented. One QTL was limited to a single gene. The gene in question displayed a nucleotide signature indicative of balancing selection, and its phenotypic effects are reversed depending on genetic background. If this region typifies many complex trait loci, then non-neutral epistatic polymorphism may be an important contributor to genetic variation in complex traits.     

Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design

Heterosis represents one of the most revolutionary advancements in crop improvement.In the genetic dissection of heterosis,NCIII design is one of the most powerful and widely used mating schemes.However,the methodologies for quantitative trait loci (QTL) detection in the design were mostly based on composite interval mapping.Therefore,in this study,our purpose was to develop a statistical method for mapping epistatic QTL associated with heterosis in the RIL-based NCIII design.First,we derivated the expectations of two classical linear transformations,Z 1 and Z 2,while a quantitative trait was controlled by two QTL with digenic epistasis and arbitrary linkage under the F ∞ and F 2 metric models.Then,we constructed an epistatic genetic model that includes all markers on the whole genome simultaneously,and estimated all the parameters in the model by the empirical Bayes approach.Finally,a series of Monte Carlo simulation experiments was carried out to confirm the proposed approach.The results show that:(1) all the augmented genetic parameters for main-effect QTL could be rightly identified with satisfactory statistical power and precision;(2) the statistical powers in the detection of augmented epistatic effects were substantively affected by the signs of pure epistatic effects;(3) it is more difficult to detect epistatic QTL than to detect main-effect QTL;(4) statistical power is higher in the RIL-based NCIII design than in the F 2-based NCIII design,especially in the detection of the augmented epistatic effect that consists of two pure epistatic effects in opposite directions.     

Genetic contributions to stability and change in intelligence from childhood to old age

Understanding the determinants of healthy mental ageing is a priority for society today. So far, we know that intelligence differences show high stability from childhood to old age and there are estimates of the genetic contribution to intelligence at different ages. However, attempts to discover whether genetic causes contribute to differences in cognitive ageing have been relatively uninformative. Here we provide an estimate of the genetic and environmental contributions to stability and change in intelligence across most of the human lifetime. We used genome-wide single nucleotide polymorphism (SNP) data from 1,940 unrelated individuals whose intelligence was measured in childhood (age 11 years) and again in old age (age 65, 70 or 79 years). We use a statistical method that allows genetic (co)variance to be estimated from SNP data on unrelated individuals. We estimate that causal genetic variants in linkage disequilibrium with common SNPs account for 0.24 of the variation in cognitive ability change from childhood to old age. Using bivariate analysis, we estimate a genetic correlation between intelligence at age 11 years and in old age of 0.62. These estimates, derived from rarely available data on lifetime cognitive measures, warrant the search for genetic causes of cognitive stability and change.     

A genome scan for quantitative trait loci affecting grain yield and its components of maize both in single-and two-locus levels

Maize is one of the most important cereal crops in the world. The hybrid yield advantage is responsible for about 10 percent of the total global maize production of 550 Mt[1]. It is exigent to study the yield traits so as to improve the hybrids per se in …     

An SNP map of human chromosome 22

The human genome sequence will provide a reference for measuring DNA sequence variation in human populations. Sequence variants are responsible for the genetic component of individuality, including complex characteristics such as disease susceptibility and drug response. Most sequence variants are single nucleotide polymorphisms (SNPs), where two alternate bases occur at one position. Comparison of any two genomes reveals around 1 SNP per kilobase. A sufficiently dense map of SNPs would allow the detection of sequence variants responsible for particular characteristics on the basis that they are associated with a specific SNP allele. Here we have evaluated large-scale sequencing approaches to obtaining SNPs, and have constructed a map of 2,730 SNPs on human chromosome 22. Most of the SNPs are within 25 kilobases of a transcribed exon, and are valuable for association studies. We have scaled up the process, detecting over 65,000 SNPs in the genome as part of The SNP Consortium programme, which is on target to build a map of 1 SNP every 5 kilobases that is integrated with the human genome sequence and that is freely available in the public domain.