Using newly developed methods and software, association mapping was conducted for chromium content and total sugar in tobacco leaf, based on four-omics datasets. Our objective was to collect data on genotype and phenotype for 60 leaf samples at four developmental stages, from three plant architectural positions and for three cultivars that were grown in two locations. Association mapping was conducted to detect genetic variants at quantitative trait SNP(QTS) loci, quantitative trait transcript(QTT) differences,quantitative trait protein(QTP) variability, and quantitative trait metabolite(QTM) changes,which can be summarized as QTX locus variation. The total heritabilities of the four-omics loci for both traits tested were 23.60% for epistasis and 15.26% for treatment interaction.Epistasis and environment × treatment interaction had important impacts on complex traits at all-omics levels. For decreasing chromium content and increasing total sugar in tobacco leaf, six methylated loci can be directly used for marker-assisted selection, and expression of ten QTTs, seven QTPs and six QTMs can be modified by selection or cultivation.
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.
A promising way to uncover the genetic architectures underlying complex traits may lie in the ability to recognize the genetic variants and expression transcripts that are responsible for the traits' inheritance.However,statistical methods capable of investigating the association between the inheritance of a quantitative trait and expression transcripts are still limited.In this study,we described a two-step approach that we developed to evaluate the contribution of expression transcripts to the inheritance of a complex trait.First,a mixed linear model approach was applied to detect significant trait-associated differentially expressed transcripts.Then,conditional analysis were used to predict the contribution of the differentially expressed genes to a target trait.Diallel cross data of cotton was used to test the application of the approach.We proposed that the detected differentially expressed transcripts with a strong impact on the target trait could be used as intermediates for screening lines to improve the traits in plant and animal breeding programs.It can benefit the discovery of the genetic mechanisms underlying complex traits.
YANG DaiGangYE ChengYinMA XiongFengZHU ZhiHongZHOU XiaoJianWANG HaiFengMENG QingQinPEI XiaoYuYU ShuXunZHU Jun
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/.
To detect genes underlying anxiety-related traits in mice,we performed univariate and multivariate QTL mapping analyses of phenotypes obtained from 71 mice of the BXD recombinant inbred (RI) strains (n=528 mice) and their parental strains (C57BL/6J and DBA/2J).Separate and joint mapping analyses were carried out using a linkage map composed of 506 simple sequence repeats (SSRs).The main QTL effects,interactions between pairs of QTLs (epistasis),and their environmental interactions were estimated.The results showed that anxiety-related traits were influenced by multiple QTLs (five main effect QTLs and three epistatic QTLs).Ten potential anxiety-related candidate genes within the QTL intervals on chromosomes 5,13 and 15 were identified.Some of these genes have been reported previously to be associated with the anxiety response.Based on our results,it is suggested that the multivariate QTL mapping approach improves the statistical power for detecting QTL and the precision of parameter estimation.Moreover,multivariate mapping can also detect pleiotropic QTL effects.
ZHU ZhiHongZHANG ChenHaoWANG XuShengCOOK Melloni NWILLIAMS RobertLU LuZHU Jun
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.
Most important agronomic and quality traits of crops are quantitative in nature.The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs),and the interactions between QTLs and the environment.It is crucial to understand the genetic architecture of complex traits to design efficient strategies for plant breeding.In the present study,a new experimental design and the corresponding statistical method are presented for QTL mapping.The proposed mapping population is composed of double backcross populations derived from backcrossing both homozygous parents to DH (double haploid) or RI (recombinant inbreeding) lines separately.Such an immortal mapping population allows for across-environment replications,and can be used to estimate dominance effects,epistatic effects,and QTL-environment interactions,remedying the drawbacks of a single backcross population.In this method,the mixed linear model approach is used to estimate the positions of QTLs and their various effects including the QTL additive,dominance,and epistatic effects,and QTL-environment interaction effects (QE).Monte Carlo simulations were conducted to investigate the performance of the proposed method and to assess the accuracy and efficiency of its estimations.The results showed that the proposed method could estimate the positions and the genetic effects of QTLs with high efficiency.
Rice (Oryza sativa) was first domesticated in the lower and middle Yangtze regions of China, and rice remains have been found in many Chinese archaeological sites. Until now, only phenotypic archeobotanical evidence, such as the spikelet bases of ancient grains, has been used to speculate on the domestication process and domestication rate of rice. In this study, we sequenced 4 genomic segments from rice remains in Tianluoshan, a site of the local Hemudu Neolithic culture in the low Yangtze and two other archaeological sites (~2400 and 1200 BC, respectively). We compared our sequences with those of the current domesticated and wild rice (O. rufipogon) populations. At least two genotypes were found in the remains from each site, suggesting a heterozygotic state of the rice seeds. One ancient genotype was not found in the current domesticated population and might have been lost. The rice remains belonged to the japonica group, and most if not all were japonica-type, suggesting that the remains might be at an early stage of indica-japonica divergence or an indica-japonica mixture. We also identified sequences with significant similarity to those from species of Sapindales, Zygophyllales, and Brassicales, which is consistent with the identification of other plant remains in the Tianluoshan site and the common rice field weeds such as mustards in southern China.
FAN LongJiangGUI YiJieZHENG YunFeiWANG YuCAI DaGuangYOU XiuLing
