基于稳定氧同位素确定植物水分来源不同方法的比较

利用稳定同位素技术确定植物水分来源,对提高生态水文过程的认识和对干旱半干旱区的生态管理至关重要。目前基于稳定同位素技术确定植物水分来源的方法众多,但不同方法之间对比的研究较少。本研究基于原位样品采集,室内实验测试,利用直接对比法、多元线性混合模型(IsoSource)、贝叶斯混合模型(MixSIR、MixSIAR)和吸水深度模型分析植物水分来源,并对比各方法的优缺点。结果表明:相对于多元线性混合模型(IsoSource)而言,贝叶斯混合模型(MixSIR、MixSIAR)具有更好的水源区分性能,但对数据要求较高,且植物木质部水和潜在水源同位素组成的标准差越小,模型运行结果的可信度更高。本研究中贝叶斯混合模型(MixSIR)为最优解。在利用稳定氢氧同位素技术确定植物水分来源时,可先通过直接对比法定性判断植物可能利用的潜在水源,然后再用多元线性混合模型(IsoSource)、贝叶斯混合模型(MixSIR、MixSIAR)计算出各潜在水源对植物的贡献率和贡献范围,必要时可评估模型性能,选择出最优模型,定量分析植物的水分来源。若植物主要吸收利用不同土层深度的土壤水,可结合吸水深度模型计算出植物...     

空气污染对信鸽比赛个体归巢速度的影响

空气污染作为一种有害的环境因素,对人类及动物的生理、心理均有影响.在鸟类中,信鸽(Columba livia)是研究空气污染影响的理想模型.为探究空气污染的行为学效应,通过收集并筛选2018和2019年成都市信鸽协会春秋两个季节举办的64场赛事共285羽参赛5场及以上的信鸽不同空距等级下的归巢速度,利用混合线性模型分析...     

利用线性混合效应模型模拟杉木人工林枝条生物量

基于福建省将乐林场45株人工杉木解析木的572组枝条生物量数据,采用线性混合效应模型方法,建立杉木人工林枝条总生物量和枝、叶生物量的预测模型,并利用独立样本数据对模型进行检验.结果表明： 线性混合效应模型比传统多元线性回归模型的拟合精度高.不同随机效应参数的组合,其混合模型的精度不同.考虑异方差结构的混合模型能够消除数据间的异方差性,其精度更高,其中,对于枝条总生物量和叶生物量模型,以指数函数作为异方差结构时的模型精度最高;对于枝生物量模型,以常数加幂函数作为异方差结构时的模型精度最高.模型检验结果表明：对于杉木人工林枝条生物量预测模型,考虑随机效应和异方差结构的线性混合模型的检验精度比传统多元线性回归模型的精度有明显提高.     

广义岭回归在家禽育种值估计中的应用

讨论了岭回归方法应用于混合线性模型方程组中估计家禽育种值的方法,其实质是将传统的混合线性模型方程组理解为一种广义岭回归估计,为确定遗传参数的估计提供了一种途径;同时,以番鸭为例,考虑了一个性状和两个固定效应,采用广义岭回归法对公番鸭育种值进行了估计,并与最佳线性无偏预测法(BLUP 法)进行了比较,结果表明,广义岭回归方法和BLUP 法估计的育种值及其排序非常接近,其相关系数和秩相关系数分别达到了0.998~(**)和0.986~(**),且采用广义岭回归法预测的误差率低(在±10%以内);表明在混合线性模型方程组中使用广义岭回归估计动物育种值的方法具有可行性,并可省去估计遗传参数的过程,使BLUP 法在动物选育中的应用更具实用性.     

基于线性混合模型的红松人工林一级枝条大小预测模拟

基于黑龙江省孟家岗林场60株人工红松955个标准枝数据,采用线性混合效应模型理论和方法,考虑树木效应,利用SAS软件中的MIXED模块拟合红松人工林一级枝条各因子(基径、枝长、着枝角度)的预测模型.结果表明： 通过选择合适的随机参数和方差协方差结构能够提高模型的拟合精度;把相关性结构包括复合对称结构CS、一阶自回归结构AR(1)及一阶自回归与滑动平均结构ARMA(1,1)加入到一级枝条大小最优混合模型中,AR(1)可显著提高枝条基径和角度混合模型的拟合精度,但3种结构均不能提高枝条角度混合模型的精度.为了描述混合模型构建过程中产生的异方差现象,把CF₁和CF₂函数加入到枝条混合模型中,CF₁函数显著提高了枝条角度混合模型的拟合效果,CF₂函数显著提高了枝条基径和长度混合模型拟合效果.模型检验结果表明：对于红松人工林一级枝条大小预测模型,混合效应模型的估计精度比传统回归模型估计精度明显提高.
     

中国东南部不同海拔亚热带森林中马尾松径向生长对气候的响应

为了解我国东南部亚热带森林不同海拔树木生长对气候响应的差异,建立了福建省武夷山脉东麓2个样点的4个马尾松(Pinus massoniana)轮宽年表,对树木径向生长与气候因子进行了bootstrapped相关分析和线性混合模型(LME)拟合。结果表明,在高海拔地区马尾松径向生长对气候因子年际波动敏感性较强,主要表现为与生长季前冬季光温条件以及生长季内7月降水的正相关,生长-气候关系在不同样点间表现出较强的一致性。线性混合模型可以较好地拟合高海拔树木生长变化,当使用前1年12月平均日最高温、当年1月日照时长和当年7月降雨量3个气候变量进行拟合时,模型解释量达到0.5,其中前1年12月最高温和当年1月日照时数在模型中起到主导作用,累积相对贡献率约占80%,说明生长季前冬季的光热条件是限制高海拔马尾松径向生长最主要的气候因子。因此,我国亚热带地区高海拔的树木径向生长可能对未来气候变化有更强的敏感性,相关森林管理政策的制定需要将此纳入考虑;同时我国亚热带地区高海拔森林中的树木有被用于树轮气候重建的潜力。     

基于混合效应模型的人工红松节子属性

基于黑龙江省孟家岗林场60株人工红松1534个节子数据,利用SAS软件中的NLMIXED和GLIMMIX模块构建人工红松节子属性因子(基径、健全节长度、死亡年龄、角度)的混合效应预测模型.采用赤池信息准则(AIC)、贝叶斯信息准则(BIC)、对数似然值(-2LL)和似然比检验(LRT)评价指标对所构建模型的精度进行比较.结果表明:考虑树木效应的混合模型模拟精度均高于传统回归模型.含有b_1、b_2随机参数组合的节子基径模型是最优混合效应模型;含有b_1、b_3随机参数组合的节子健全节长度模型是最优混合效应模型;含有节子基径随机参数的广义线性混合模型为节子死亡年龄的最优模型;含有截距、节子基径、健全节长度3种随机效应参数组合的广义线性混合模型为节子角度的最优模型.混合效应模型比传统回归模型更能有效地描述节子属性.红松是东北主要的用材树种,利用节子属性预测模型结合合理的整枝方案可以提高木材质量.     

浙江沿岸春秋季小黄鱼生长异质性

为更精准地掌握浙江沿岸春秋季小黄鱼生长动态,本文利用2014—2019年春季(4月)和秋季(11月)在浙江沿岸海域底拖网调查资料,通过构建一个广义线性模型(GLM)和9个线性混合效应模型(LMEM)来研究小黄鱼生长的异质性。结果表明:小黄鱼平均体长为124.12 mm(15～210 mm),优势组为110～140 mm;平均体重为33.28 g(0.04～156.2 g),优势组为30～50 g。根据AIC最小准则,同时具有季节和水域对生长参数a、b随机效应的LMEM模型最优,且交叉验证的结果也表明此模型的预测效果最佳。在最优模型中,生长参数a的固定值为0.61×10^-4,加入季节和水域随机效应后a值为0.32×10^-4～1.91×10^-4,b的固定值为2.73,加入季节和水域随机效应后b值范围为2.49～2.86,表明小黄鱼为负异速生长,季节和水域对小黄鱼体长与体重关系有显著影响。从季节上来看,春季小黄鱼生长速度快于秋季,从水域分布来看,离岸距离越短的水域小黄鱼生长速度越快。     

人工红松树干内部节子体积预测模型

基于黑龙江省林口林业局林场和东京城林业局林场29块标准地中49株人工红松1207个节子数据,使用图片处理软件Digimizer对节子纵剖面图片数据进行提取,将节子形状用一个二维散点图表示。根据节子二维形状散点图,把人工红松节子分为3种类型: 活节(整个节子为健全节)、未包藏死节(节子由健全节和疏松节组成)和包藏死节(节子的健全节和疏松节部分被树干包藏)。3个类型节子的健全节体积通过对健全节形状参数方程求积得到;疏松节体积利用圆柱体的体积计算得到;节子总体积等于健全节体积与疏松节体积之和。最后,基于节子变量(节子直径、节子相对高、节子总长度)和树木变量(胸径),采用样地和树木水平的线性混合模型建立了红松人工林健全节体积、疏松节体积和节子总体积的预测模型。与基础模型相比,考虑样地和树木水平的混合效应所建立的健全节体积、疏松节体积和节子总体积预测模型,其参数估计更精准,残差分布更均匀,拟合精度明显提高。检验结果表明,基础模型预估精度均在90%以上,引入样地和树木效应的混合模型的预估精度均在93%以上,说明所建模型可以很好地预测红松人工林节子体积大小。     

玉米马铃薯秸秆混合腐解的非加性效应

有机残体混合分解对陆地生态系统物质循环至关重要,但关于农田生态系统中混合秸秆分解过程的研究仍较缺乏。该研究在玉米(Zea mays)单作、马铃薯(Solanum tuberosum)单作和玉米马铃薯间作小区实验中,设置了为期6个月的玉米秸秆、马铃薯秸秆和玉米马铃薯混合秸秆分解袋填埋实验,通过Biolog-Eco微平板法分析秸秆类型和分解环境对秸秆微生物碳代谢活性的影响。结果表明,马铃薯秸秆和玉米秸秆混合对分解过程产生了协同效应,混合秸秆的分解率和微生物代谢活性高于单一秸秆,增加了微生物对碳水化合物和羧酸类底物的利用。这种协同效应随时间延长而削弱。随机森林模型和结构方程模型分析表明,土壤中溶解性有机碳、硝态氮、铵态氮含量以及秸秆碳氮比是驱动秸秆分解的重要因素。总之,秸秆混合促进秸秆分解。     

GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction

Genome-wide association study (GWAS) and genomic prediction/selection (GP/GS) are the two essential enterprises in genomic research. Due to the great magnitude and complexity of genomic and phenotypic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely-used genomic association and prediction integrated tool as an R package. The first version was released to the public in 2012 with the implementation of the general linear model (GLM), mixed linear model (MLM), compressed MLM (CMLM), and genomic best linear unbiased prediction (gBLUP). The second version was released in 2016 with several new implementations, including enriched CMLM (ECMLM) and settlement of MLMs under progressively exclusive relationship (SUPER). All the GWAS methods are based on the single-locus test. For the first time, in the current release of GAPIT, version 3 implemented three multi-locus test methods, including multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). Additionally, two GP/GS methods were implemented based on CMLM (named compressed BLUP; cBLUP) and SUPER (named SUPER BLUP; sBLUP). These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS, but also improve computing speed and increase the capacity to analyze big genomic data. Here, we document the current upgrade of GAPIT by describing the selection of the recently developed methods, their implementations, and potential impact. All documents, including source code, user manual, demo data, and tutorials, are freely available at the GAPIT website (http://zzlab.net/GAPIT).     

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.     

Genome-wide association study of shank length and diameter at different developmental stages in chicken F2 resource population

In order to find SNPs and genes affecting shank traits, we performed a GWAS in a chicken F2 population of eight half-sib families from five hatches derived from reciprocal crosses between an Arian fast-growing line and an Urmia indigenous slow-growing chicken. A total of 308 birds were genotyped using a 60K chicken SNP chip. Shank traits including shank length and diameter were measured weekly from birth to 12 weeks of age. A generalized linear model and a compressed mixed linear model (CMLM) were applied to achieve the significant regions. The value of the average genomic inflation factor (λ statistic) of the CMLM model (0.99) indicated that the CMLM was more effective than the generalized linear model in controlling the population structure. The genes surrounding significant SNPs and their biological functions were identified from NCBI, Ensembl and UniProt databases. The results indicated that 12 SNPs at 12 different ages passed the LD-adjusted 5% Bonferroni significant threshold. Two SNPs were significant for shank length and nine SNPs were significant for shank diameter. The significant SNPs were located near to or inside 11 candidate genes. The results showed that a number of significant SNPs in the middle ages were higher than the rest. The MXRA8 gene was related to the significant SNP at week 1 that promotes proliferation of growth plate chondrocytes. A unique SNP of Gga_rs16689511 located on chicken Z chromosome within the LOC101747628 gene was related to shank length at three different ages of birds (weeks 8, 9 and 11). The significant SNPs for shank diameter were found at weeks 4 and 7 (four and five SNPs respectively). The identifications of SNPs and genes here could contribute to a better understanding of the genetic control of shank traits in chicken.     

Genome‐wide association of drought‐related and biomass traits with HapMap SNPs in Medicago truncatula

Improving drought tolerance of crop plants is a major goal of plant breeders. In this study, we characterized biomass and drought‐related traits of 220 Medicago truncatula HapMap accessions. Characterized traits included shoot biomass, maximum leaf size, specific leaf weight, stomatal density, trichome density and shoot carbon‐13 isotope discrimination (δ¹³C) of well‐watered M. truncatula plants, and leaf performance in vitro under dehydration stress. Genome‐wide association analyses were carried out using the general linear model (GLM), the standard mixed linear model (MLM) and compressed MLM (CMLM) in TASSEL, which revealed significant overestimation of P‐values by CMLM. For each trait, candidate genes and chromosome regions containing SNP markers were found that are in significant association with the trait. For plant biomass, a 0.5 Mbp region on chromosome 2 harbouring a plasma membrane intrinsic protein, PIP2, was discovered that could potentially be targeted to increase dry matter yield. A protein disulfide isomerase‐like protein was found to be tightly associated with both shoot biomass and leaf size. A glutamate‐cysteine ligase and an aldehyde dehydrogenase family protein with Arabidopsis homologs strongly expressed in the guard cells were two of the top genes identified by stomata density genome‐wide association studies analysis.     

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

Power analysis for cluster randomized trials with continuous coprimary endpoints

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K ($K\ge 2$) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed-form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.     

Genomic loci associated with antibody-mediated immune responses in an F2 chicken population

Immunity-related traits are heritable in chicken, therefore, it is possible to improve the inherent immunity by breeding programs. In this study using the Illumina chicken 60K single nucleotide polymorphisms (SNPs) chip, we performed a set of genome-wide association studies to determine candidate genes and loci responsible for primary and secondary antibody-mediated responses against sheep red blood cell. A F2 population descended from a commercial meat-type breed and an Iranian indigenous chicken was used for this study. Statistical analysis was based on a mixed linear model utilizing genomic relationship matrix to prevent spurious associations. Correction for multiple testing was done by applying 5% and 10% chromosomal false discovery rates (FDRs) for significant and suggestive thresholds, respectively. Nine significant and 17 suggestive associated SNPs were identified. Most of the SNPs that were suggestively associated with the primary response of total plasma immunoglobulins were also significantly associated with this trait in secondary response. Three SNPs were located within a narrow region of 23 kb on chromosome 16. Pathway analysis for the genes surrounding the associated SNPs showed that they are involve in antigen processing and presentation, primary immunodeficiency, vitamin digestion and absorption, cell adhesion molecules, phagosome, influenza A, folding, assembly and peptide loading of class I major histocompatibility complex, lipid digestion, mobilization, and transport (FDR < 0.1). Interestingly, there were common regains associated with multiple immune-related traits.     

Discovering Candidate Chromosomal Regions Linked to Kernel Size-Related Traits via QTL Mapping and Bulked Sample Analysis in Maize

Kernel size-related traits, including kernel length, kernel width, and kernel thickness, are critical components in determining yield and kernel quality in maize (Zea mays L.). Dissecting the phenotypic characteristics of these traits, and discovering the candidate chromosomal regions for these traits, are of potential importance for maize yield and quality improvement. In this study, a total of 139 F2:3 family lines derived from EHel and B73, a distinct line with extremely low ear height (EHel), was used for phenotyping and QTL mapping of three kernel size-related traits, including 10-kernel length (KL), 10-kernel width (KWid), and 10-kernel thickness (KT). The results showed that only one QTL for KWid, i.e., qKWid9 on Chr9, with a phenotypic variation explained (PVE) of 13.4% was detected between SNPs of AX-86298371 and AX-86298372, while no QTLs were detected for KL and KT across all 10 chromosomes. Four bulked groups of family lines, i.e., Groups I to IV, were constructed with F2:3 family lines according to the phenotypic comparisons of KWid between EHel and B73. Among these four groups, Group I possessed a significantly lower KWid than EHel (P = 0.0455), Group II was similar to EHel (P = 0.34), while both Group III and Group IV were statistically higher than EHel (P < 0.05). Besides, except Group IV exhibited a similar KWid to B73 (P = 0.11), KWid of Groups I to III were statistically lower than B73 (P < 0.00). By comparing the bulked genotypes of the four groups to EHel and B73, a stable chromosomal region on Chr9 between SNPs of AX-86298372 to AX-86263154, entirely covered by qKWid9, was identified to link KWid with the positive allele of increasing phenotypic effect to KWid from B73, similar to that of qKWid9. A large amount of enzyme activity and macromolecule binding-related genes were annotated within this chromosomal region, suggesting qKWid9 as a potential QTL for KWid in maize.     

A genome‐wide association study suggests several novel candidate genes for carcass traits in Chinese Simmental beef cattle

A genome‐wide association study (GWAS) was conducted for two carcass traits in Chinese Simmental beef cattle. The experimental population consisted of 1301 individuals genotyped with the Illumina BovineHD SNP BeadChip (770K). After quality control, 671 990 SNPs and 1217 individuals were retained for the GWAS. The phenotypic traits included carcass weight and bone weight, which were measured after the cattle were slaughtered at 16 to 18 months of age. Three statistical models—a fixed polygene model, a random polygene model and a composite interval mapping polygene model—were used for the GWAS. The genome‐wide significance threshold after Bonferroni correction was 7.44E‐08 (= 0.05/671 990). In this study, we detected eight and seven SNPs significantly associated with carcass weight and bone weight respectively. In total, 11 candidate genes were identified within or close to these significant SNPs. Of these, we found several novel candidate genes, including PBX1, GCNT4, ALDH1A2, LCORL and WDFY3, to be associated with carcass weight and bone weight in Chinese Simmental beef cattle, and their functional roles need to be verified in further studies.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.