An Expanded View of Complex Traits:From Polygenic to Omnigenic

A central goal of genetics is to understand the links between genetic variation and disease.Intuitively,one might expect diseasecausing variants to cluster into key pathways that drive disease etiology.But for complex traits,association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease.

Mapping epistasis and environment × QTX interaction based on four-omics genotypes for the detected QTX loci controlling complex traits in tobacco

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.

Joint association analysis method to dissect complex genetic architecture of multiple genetically related traits

Genome-wide association study(GWAS)has been a standard approach to discover the genetic determinants underlying complex traits.It is a major challenge in GWAS how to improve analysis power,uncover complex genetic correlation,and reveal gene-gene and gene-environment interactions through integrated analysis of multiple genetically related traits.To combat these challenges,we proposed a mixed linear model-based joint association analysis method for multiple traits,which include epistasis and geneenvironment interaction in the mapping model and utilize within-trait variance and between-trait covariance simultaneously;A F-statistics based on Wilks statistics is used to test the significance of each SNP and paired interacted SNPs,each genetic effects of QTS are estimated and tested by the MCMC method based on a QTS full model.Simulations showed that the multi-trait GWAS method could provide increased power in detecting pleiotropic loci affecting more than one trait,and can unbiasedly estimate effects of QTS.To demonstrate the performance of the proposed method,we analyzed four blood lipid traits in Multi-Ethnic Study of Atherosclerosis(MESA)Cohort and two yield-related traits in a rice immortalized F2 dataset.A software package was developed for the proposed method.

Assessment of the Characteristics of Demersal Fish Communities Using Species-and Trait-Based Approaches in the Coastal Habitats Along Rongcheng Bay,Shandong Peninsula,China

Biodiversity declines have motivated many studies on the relationship between species diversity and ecosystem functioning.In this study,we described the spatial-temporal characteristics of demersal fish communities along a coastal habitat in Rongcheng Bay,Shandong Peninsula,China with both species-based and biological trait-based approaches.The field survey was carried out monthly using traps from April to October of 2018,and divided into three seasons(spring:April and May;summer:June,July and August;autumn:September,October and November).The study area included five distinct habitats:seagrass bed,natural rocky reef,bare sand,artificial reef together with natural rocky reef,and artificial reef together with bare sand.We analyzed the fish communities with three taxonomic diversity indices,including Shannon-Wiener,Simpson,and Pielou Evenness,as well as four functional diversity indices,including FRic,FEve,FDiv,and FDis,based on 7 functional groups which are categorized into 27 traits.The results showed that there were no significant differences in taxonomic diversity indices among different habitats in the three seasons.However,significant differences were found in the functional richness of fish communities among different habitats in three seasons.Seagrass bed represented the highest functional richness in spring and autumn.This study demonstrates that seagrass bed is very important in enhancing the functional diversity of fish communities in a complex habitat.The study also indicates that the combination of taxonomic diversity and functional diversity will provide a more detailed description of the characteristics of fish communities.

A Preliminary Study of Mapping Genes Underlying Complex Traits Based on Chromosome Segment Substitution Lines

Complex traits are the features whose properties are determined by multiple factors, which can be genetic or environmental. Most of economically important characteristics of plants and animals belong to this special catego-

Influence of outliers on QTL mapping for complex traits

A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus (QTL) mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates (FDRs) of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additive× environment interaction effects of QTLs in the presence of outliers.

Advances in the Research of Strategies and Methods for Analyzing Complex Traits

Complex traits are the features whose properties are determined by both genetic and environmental factors. Generally, complex traits include the classical quantitative traits with continuous distribution, the binary or categorical traits with discrete distribution controlled by polygene and other traits that cannot be measured exactly, such as behavior and psychology. Most human complex diseases and most economically important traits in plants and animals belong to the category. Understanding the molecular basis of complex traits plays a vital role in the genetic improvement of plant and animal breeding. In this article, the conception and research background of complex traits were summarized, and the strategies, methods and the great progress that had been made in dissecting genetic basis of complex traits were reviewed. The challenges and possible developments in future researches were also discussed.

Efficient protocols and methods for high-throughput utilization of the Collaborative Cross mouse model for dissecting the genetic basis of complex traits

The Collaborative Cross(CC)mouse model is a next‐generation mouse genetic reference population(GRP)designated for a high‐resolution quantitative trait loci(QTL)mapping of complex traits during health and disease.The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild‐derived strains.Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions.In this article,we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases,including type 2 diabetes and metabolic diseases,body composition,immune response,colorectal cancer,susceptibility to Aspergillus fumigatus,Klebsiella pneumoniae,Pseudomonas aeruginosa,sepsis,and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum,which were conducted at our laboratory using the CC mouse population.These traits are observed at multiple levels of the body systems,including metabolism,body weight,immune profile,susceptibility or resistance to the development and progress of infectious or chronic diseases.Herein,we present full protocols and step‐by‐step methods,implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines,mapping the gene underlying the host response to these infections and chronic diseases.The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits,including chronic and infectious diseases.

烟草生物碱性状的QTL定位

生物碱是烟草的重要化学成分。为明确烟草生物碱的遗传结构,发掘控制相关性状的主效位点,以烟草品种Y3、K326为亲本,构建大小为271的重组自交系群体。分别于2018、2019和2020年在云南省昆明市石林、玉溪市研和种植群体材料,检测总植物碱(TPA)、烟碱(NIC)、降烟碱(NOR)、假木贼碱(ANAB)和新烟草碱(ANAT)5种生物碱表型。对群体进行基因组测序,构建包含46,129个标记的遗传连锁图谱。利用基于混合线性模型的QTL定位方法及软件QTLNetwork2.0,进行QTL定位分析。共定位15个具有显著加性效应的QTL,加性效应对表型贡献率为0.58%~11.57%。其中4个主效QTL即控制总植物碱的qTPA14、烟碱的qNIC14、假木贼碱的qANAB14和新烟草碱的qANAT14,均可以解释相应性状10%以上的表型变异,且均位于14号连锁群上。6个QTL具有显著的加性与环境互作效应,对表型贡献率为0.80%~1.81%。5对QTL具有显著加性-加性上位性效应,对表型的贡献率为0.15%~2.31%。2对QTL具有显著的上位性与环境互作效应,对表型的贡献率为0.81%~1.16%。研究结果为进一步分离候选基因、解析遗传机理和促进烟草生物碱性状分子改良奠定了基础。

棉籽油性状全基因组关联分析研究进展

棉籽油味道柔和,色泽金黄,在各种食品中起着至关重要的作用。此外,棉籽油因其独特的脂肪酸特性、抗炎和心脏保护特性而备受关注。因此,了解调控棉籽贮藏油生物合成的遗传机制,开展关键基因挖掘,选育高油优质棉花品种具有重要意义。随着高通量测序技术和生物信息学分析工具的快速发展,全基因组测序变得更加简单高效,使得全基因组关联分析技术(GWAS)成为了研究复杂性状的一种核心工具。介绍了GWAS的原理及研究优势,论述GWAS在不同油料作物及棉花性状研究中的应用进展。并对今后棉籽油相关性状的遗传研究提出了一些建议,对存在的问题进行探讨及展望,旨在为今后利用GWAS进行棉籽油性状遗传基础的研究及分子标记辅助育种提供理论依据。

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions.Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate.Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions.We found that latituderelated environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits.For the 23 traits,we detected 109 quantitative trait loci(QTLs),29 for mean values,66 for plasticity,and 14 for both parameters,and 80%of the QTLs interacted with latitude.The effects of several QTLs changed in magnitude or sign,driving variation in phenotypic plasticity.We experimentally validated one plastic gene,ZmTPS14.1,whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway.By integrating genetic diversity,environmental variation,and their interaction into a joint model,we could provide site-specific predictions with increased accuracy by as much as 9.9%,2.2%,and 2.6%for days to tassel,plant height,and ear weight,respectively.This study revealed a complex genetic architecture involving multiple alleles,pleiotropy,and genotype-byenvironment interaction that underlies variation in the mean and plasticity of maize complex traits.It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments,paving a practical way toward precision agriculture.

Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms

Alternative splicing exists in most multi-exonic genes,and exploring these complex alternative splicing events and their resultant isoform expressions is essential.However,it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions.Transcript-level quantification and interpretation are often overlooked,and biological interpretations are often deduced based on combined transcript information at the gene level.Here,for the most variable tissue of alternative splicing,the brain,we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression(GTEx)Consortium using a powerful method that we previously developed.We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci(irQTL),which could not be detected by studying gene-level expressions alone.By analyzing the genetic architecture of the irQTL,we show that isoform ratios regulate edu-cational attainment via multiple tissues including the frontal cortex(BA9),cortex,cervical spinal cord,and hippocampus.These tissues are also associated with different neuro-related traits,including Alzheimer’s or dementia,mood swings,sleep duration,alcohol intake,intelligence,anxiety or depression,etc.Mendelian randomization(MR)analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships,showing much stronger causal effects than on general diseases measured in the UK Biobank(UKB).Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases,which could be missed by merely investigating overall gene expressions.

Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

Many rice-growing areas are affected by high concentrations of arsenic(As).Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health.Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits.We explored the predictive ability(PA)of genomic prediction with single-environment models,accounting or not for trait-specific markers,multi-environment models,and multi-trait and multi-environment models,using the genotypic(1600K SNPs)and phenotypic(grain As content,grain yield and days to flowering)data of the Bengal and Assam Aus Panel.Under the base-line single-environment model,PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content,respectively;the three prediction methods(Bayesian Lasso,genomic best linear unbiased prediction and reproducing kernel Hilbert spaces)were considered to perform similarly,and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17K,without negative effect on PA of genomic predictions.Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%.Multi-environment models,accounting for genotype×environment interactions,and multi-trait and multi-environment models outperformed the base-line models by up to 47%and 61%,respectively.Among the multi-trait and multi-environment models,the Bayesian multi-output regressor stacking function obtained the highest predictive ability(0.831 for grain As)with much higher efficiency for computing time.These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel.Genomic prediction can also be applied to breeding for other complex traits under multiple environments.

全基因组关联分析中混合模型的原理、优化与应用

全基因组关联分析(genome-wide association study,GWAS)是定位基因组中与性状显著关联的变异位点的有效方法。随着表型记录的完善、高通量基因型分型技术的发展,以及统计方法的改进,全基因组关联分析在人类疾病、动物植物遗传等领域得到了广泛的应用。假阳性是影响全基因组关联分析结果可靠性的重要因素之一。为了控制假阳性,除了校正P值,GWAS模型从最简单的方差分析(或用于质量性状的卡方检验)到加入固定效应协变量的普通线性模型(general linear model,GLM),再到加入随机效应的混合线性模型(mixed linear model,MLM)持续改进,控制了多种混杂因素导致的假阳性。将个体的遗传效应拟合为由基因组亲缘关系矩阵(genomic relationships matrix,GRM)定义的随机效应是目前常用的方法。由于MLM的参数估计大量消耗计算资源,研究人员不断尝试模型求解优化和GRM的构建优化(GRM的构建优化同时也提高了计算效率),最终将基于MLM计算的时间复杂度由O(MN3)逐步改进到O(MN),实现了计算速度与统计功效的飞跃。针对质量性状病例对照比失衡带来的假阳性问题,研究人员进一步对广义混合线性模型(generalized linear mixed model,GLMM)进行了校正。本文较全面地介绍了GWAS的基本原理和发展,着重阐述了GWAS中MLM模型的改进和优化细节,同时,列举了GWAS在农业中的应用,包括在植物、动物和微生物方面的研究成果,以及基于单倍型的GWAS应用。最后,从进一步提高GWAS统计功效和GWAS试验设计2个角度对GWAS未来的发展进行了展望。

Quantitativegeneticanalysisstationforthegeneticanalysisofcomplextraits

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/.

A new approach to dissecting complex traits by combining quantitative trait transcript (QTT) mapping and diallel cross analysis

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.

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.

拟南芥株高性状全基因组上位互作网络构建

【目的】以拟南芥株高性状及上位性网络模型为研究基础,通过构建不同层次的互作调控网络,探究、揭示植物生长发育过程中多基因在复杂网络中相互作用的过程或规律。【方法】以拟南芥的84个重组自交系为实验材料,共获得417 495个单核苷酸位点(SNPs)及8个时间点的株高生长数据,基于功能作图方法对测序得到的不同基因型与株高性状进行关联分析后,通过结合系统生物学中模块化的概念及统计学中降维的思想,在常微分方程组的基础上构建稀疏、有向、可量化的模块以及基因之间的上位性互作网络,同时使用拟南芥在线数据库对不同功能模块中的候选基因进行富集分析与功能注释。【结果】研究结果表明,在宏观遗传调控网络中,大部分功能模块在拟南芥发育过程中起正向调控的作用,并且随时间的变化会改变互作的策略。在微观调控网络中,与拟南芥的结构发育密切相关的基因AT4G29140在网络中对其他位点都是上调作用,同时只受到与衰老有关基因的下调作用。而与维持细胞的稳态有关的基因AT4G36910不主动发挥调控作用,其功能表达非常依赖于其他基因的控制。基因AT4G22680可能通过调节RP1的表达发挥其调控的功能。【结论】本研究从关联分析与复杂网络的角度上,探究了影响拟南芥生长的上位性机制,为植物遗传结构的解析提供了新的方法和思路。

Scoring the collective effects of SNPs: association of minor alleles with complex traits in model organisms

It has long been assumed that most parts of a genome and most genetic variations or SNPs are non-functional with regard to reproductive fitness.However,the collective effects of SNPs have yet to be examined by experimental science.We here developed a novel approach to examine the relationship between traits and the total amount of SNPs in panels of genetic reference populations.We identified the minor alleles(MAs)in each panel and the MA content(MAC)that each inbred strain carried for a set of SNPs with genotypes determined in these panels.MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms,including life span,tumor susceptibility,learning and memory,sensitivity to alcohol and anti-psychotic drugs,and two correlated traits poor reproductive fitness and strong immunity.These results suggest that the collective effects of SNPs are functional and do affect reproductive fitness.

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.