中亚温带沙漠苔藓C、N、P、K化学计量特征与土壤养分和环境的关系

前期研究发现,荒漠区苔藓的化学计量特性受苔藓斑块大小、灌木和微环境的影响。研究苔藓在不同空间分布区的化学计量特性,对理解苔藓的生长和适应策略具有重要意义。本研究选取古尔班通古特沙漠生物结皮中的优势苔藓(齿肋赤藓,Syntrichia caninervis)和苔藓斑块下的土壤,测定了不同沙丘和采样区苔藓植物和土壤的化学计量特征与计量比。研究结果表明,除苔藓C含量,苔藓化学计量特征和土壤有效养分在沙漠不同区域显著不同。苔藓植物地上与地下部分N、P、K元素的异速生长指数分别为0.251、0.389、0.442。苔藓地上部分和地下部分的N、P异速生长指数分别为0.71和0.84。苔藓的化学计量特征在地上和地下部分分布不均。此外,苔藓N、P、K元素受年均降水量、经度和土壤养分的影响。苔藓植物的养分含量受空间分布、年均温度、年均降水量和土壤养分的影响,而苔藓的生长受N限制。本研究提供了不同空间尺度下苔藓的C、N、P、K的化学计量特征,并探讨了它们与环境变量的关系,有助于了解沙漠中N、P、K的营养模式和利用策略,以及它们对全球气候变化的潜在响应。

QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples

Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlying genes by conventional QTL fine-mapping is time-consuming and labor-intensive. Here, we devised a new method, named quantitative trait gene sequencing (QTG-seq), to accelerate QTL fine-mapping. QTGseq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. Using QTG-seq, we fine-mapped a plant-height QTL in maize (Zea mays L.), qPH7, to a 300-kb genomic interval and verified that a gene encoding an NF-YC transcription factor was the functional gene. Functional analysis suggested that qPH7-encoding protein might influence plant height by interacting with a CO-like protein and an AP2 domain-containing protein. Selection footprint ana卜 ysis indicated that qPH7 was subject to strong selection during maize improvement. In summary, QTG-seq provides an efficient method for QTL fine-mapping in the era of “big data".

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.

mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field experiments.To solve this issue,multi-locus GWAS methods have been recommended.However,only a few software packages for multi-locus GWAS are available.Therefore,we developed an R software named mr MLM v4.0.2.This software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our lab.There are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result output.The fread function in data.table is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple CPUs.In addition,the graphical user interface software mr MLM.GUI v4.0.2,built upon Shiny,is also available.To confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets.The results confirm the superior performance of mr MLM v4.0.2 to other methods currently available.False positive rates are effectively controlled,albeit with a less stringent significance threshold.mr MLM v4.0.2 is publicly available at Bio Code(https://bigd.big.ac.cn/biocode/tools/BT007077)or R(https://cran.r-project.org/web/packages/mr MLM.GUI/index.html)as an open-source software.

A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F_(2)population

Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and smalleffect genes for quantitative traits via bulked segregant analysis(BSA)in an F_(2)population.To address this issue,we proposed an integrated strategy for mapping various types of quantitative trait loci(QTLs)for quantitative traits via a combination of BSA and whole-genome sequencing.In this strategy,the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes.These observed and predicted numbers were used to construct a new statistic,G_(w),for detecting quantitative trait genes(QTGs),and the method was named dQTG-seq1.This method was significantly better than existing BSA methods.If the goal was to identify extremely over-dominant and smalleffect genes,another reserved DNA/RNA sample from each extreme phenotype F_(2)plant was sequenced,and the observed numbers of marker alleles and genotypes were used to calculate G_(w)to detect QTGs;this method was named dQTG-seq2.In simulated and real rice dataset analyses,dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods.dQTGseq2 may be extended to other heterogeneous mapping populations.The significance threshold of G_(w)in this study was determined by permutation experiments.In addition,a handbook for the R software dQTG.seq,which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html,has been provided in the supplemental materials for the users’convenience.This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F_(2)population.

ⅢVmrMLM:The R and C++tools associated with 3VmrMLM,a comprehensive GWAS method for dissecting quantitative traits

Inmost existingmethods and softwares of genome-wide association studies(GWAS)fordetecting quantitative trait nucleotides(QTNs),QTN-by-environment interactions(QEls),and QTN-by-QTN interactions(QQIs),only the allele substitution effect and its interaction-related effects are detected and estimated,conditional on method-specific polygenic background control,leading to confounding in effect estimation and insufficient polygenic background control(Li et al.,2022;Supplemental Tables 1-3).