Mapping quantitative trait loci with epistatic effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.     

Phytostabilization potential of Jatropha curcas L. in polymetallic acid mine tailings

Greenhouse pot experiments were conducted to determine the growth response, metal tolerance, and phytostabilization potential of Jatropha curcas L The plants were grown on different degrees of multi-metal contaminated acid mine soils (T0, control; T1, moderately and T2, highly contaminated soils) with or without limestone amendments. The order of metal accumulation in J. curcas was roots>stems>leaves. The higher tolerance index (>90%) with no phytotoxic symptoms and growth reduction in T1 showed that this plant has the ability to tolerate polymetallic acid mine tailings. Further, various enzymatic and non-enzymatic antioxidants also actively involved in metal defense mechanism in J. curcas. On the other hand, to alleviate the predominant phytoavailable toxic metals such as Al, Cu, and Pb, different rates (0.1, 0.25, 0.50, and 1%) of limestone amendments were added in both T1 and T2 soils. The growth performance of J. curcas was improved due to the increase in soil pH and decrease in phytoavailable soil A1 (95%), Zn (approximately 75%), and Cu (approximately 65%) contents at 0.50% of lime addition. Based on the inherent tolerance ability of J. curcas in existing adverse environmental conditions without liming, it could be used as a suitable candidate for phytostabilization in acid mine tailings.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners’ curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners’ curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.     

增温对南亚热带常绿阔叶林4种幼树生长和碳氮磷化学计量特征的影响

为了解不同物种在气候变暖条件下的生长及养分利用策略,研究选取南亚热带常绿阔叶林4种具代表性的典型乔木树种幼苗（1年生）木荷（Schima superba）、红锥（Castanopsis hystrix）、红枝蒲桃（Syzygium rehderianum）和海南红豆（Ormosia pinnata）为研究对象。采用红外-箱式增温的方法（2016年1月至2018年12月期间,年平均气温升高2.26℃,P<0.05）,研究了4种幼苗生长以及各器官中C、N、P化学计量特征和非结构性碳水化合物（NSC）含量对增温的响应。结果表明：增温促进了红锥和海南红豆（除2017年6月）的生长,但降低了木荷（增温1年后）和红枝蒲桃的生长（P<0.05）。增温显著降低了木荷细根、红锥茎干和海南红豆枝的P含量（P<0.05）,与对照相比,分别降低了1.91%、18.70%和46.07%。增温增加了固氮物种海南红豆细根N含量,但降低了其叶片N含量（P<0.05）,增温对其它树种N含量无显著影响。木荷茎干N∶P和海南红豆的细根N∶P在增温下分别升高了30.42%和90.29%（P<0.05）。此外,增温显著升高了木荷和红枝蒲桃叶片的可溶性糖含量（P<0.05）。以上结果表明,增温对南亚热带常绿阔叶林植物的影响存在种间差异,木荷和红枝蒲桃在增温条件下生长受限,但促进了红锥和海南红豆的生长,未来气候变暖可能会改变该生态系统的养分竞争平衡,进而可能会改变群落组成。     

鼎湖山南亚热带天然针阔叶混交林臭氧吸收特征

针阔叶混交林是我国南亚热带针叶林向地带性常绿阔叶林演替的中间林分类型,为我国南亚地区主要森林类型,发挥着重要的生态系统服务功能。基于树干液流技术和对臭氧浓度的连续监测,评价该森林类型的臭氧吸收特征和能力有着重要的环境生态学意义。对鼎湖山天然针阔叶混交林优势种马尾松(Pinus manssoniana)、锥栗(Castanopsis chinensis)、木荷(Schima superba)和华润楠(Machilus chinensis)在自然环境条件下的臭氧吸收能力进行了分析研究。结果表明:在日尺度上,4个优势树种的冠层气孔对臭氧导度(GO_3)和臭氧吸收通量(FO_3)均呈单峰型曲线,其最大值的时间在干季(10月至竖年3月)比湿季(4月至9月)滞后;季节尺度上,臭氧浓度在湿季达到最大值48.94 n L/L,湿季GO_3、FO_3和年臭氧吸收累积量(accumulative stomatal O_3flux,AFst)均显著高于干季(P 0.01),华润楠的臭氧吸收能力最强,在干季和湿季可分别达1.11 nmol m~(-2)s~(-1)和1.71nmol m~(-2)s~(-1)。随着水汽压亏缺(VPD)增大,优势种GO_3降低。光合有效辐射(PAR)超过1500 umol m~(-2)s~(-1)时,优势树种GO_3和FO_3呈下降趋势。针阔叶混交林的年臭氧吸收累积量超过了保护森林树木所采用的临界阈值,可认为鼎湖山针阔叶混交林受臭氧危害的潜在风险较高。     

茂名北排油页岩废渣场的土壤与植被特性研究

茂名北排油页岩废渣堆放场是一块面积达670hm²的工业生产废弃地,废渣场土壤干旱,养分贫瘠,重金属含量略为偏高,不利植物入侵与定居。这片因人为因素形成的次生裸地经过20多年的自然恢复,入侵定居植物只有24科59属66种,且大多数均为禾本科、莎草科、菊科等科的草本植物种类;草本植物有13科38属44种,占总种数的67%,占总覆盖度的80%以上。群落结构及组成种类简单,处于群落次生演替的前期阶段,表明废渣场次生裸地的植被恢复是非常缓慢的。为尽早达到控制污染、改善环境等目的,必须辅以人工措施,加速植被的恢复进程。最后提出了实现这一目的的生态恢复对策。