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 ou...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.展开更多
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 o...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.展开更多
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 pheno...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.展开更多
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 ...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-展开更多
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...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.展开更多
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 experim...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.展开更多
Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits,the absence of a universal multi-modal computational tool with robust inte...Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits,the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge.This study introduces the dual-extraction modeling(DEM)approach,a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets,enabling the prediction of complex trait phenotypes.Through comprehensive benchmarking experiments,we demonstrate the efficacy of DEM in classification and regression prediction of complex traits.DEM consistently exhibits superior accuracy,robustness,generalizability,and flexibility.Notably,we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number,underscoring its commendable interpretability.In addition,we have developed user-friendly software to facilitate seamless utilization of DEM’s functions.In summary,this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes,confirming its potential as a valuable tool for exploring the genetic basis of complex traits.展开更多
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 sequencin...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.展开更多
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 th...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.展开更多
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.H...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.展开更多
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 traditiona...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.展开更多
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 rap...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.展开更多
Understanding how gene expression is translated to phenotype is central to modern molecular biology,and the success is contingent on the intrinsic tractability of the specific traits under examination.However, an a pr...Understanding how gene expression is translated to phenotype is central to modern molecular biology,and the success is contingent on the intrinsic tractability of the specific traits under examination.However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable.Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate(S_T)by gauging the number(N) of expression states that underlie the same trait abnormality, with large S_T corresponding to large N. By analyzing over 200 yeast morphological traits, we show that S_T predicts the tractability of an expression-trait relationship. We further show that S_T is ultimately determined by natural selection, which builds co-regulated gene modules to minimize possible expression states.展开更多
全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,...全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,随着人类基因组计划和基因组单倍体图谱计划的实施,人们已通过GWAS方法发现并鉴定了大量与人类性状或复杂性疾病关联的遗传变异,为进一步了解控制人类复杂性疾病发生的遗传特征提供了重要的线索。然而,由于造成复杂性疾病/性状的因素较多,而且GWAS研究系统较为复杂,因此目前GWAS本身亦存在诸多的问题。本文将从研究方式、研究对象、遗传标记,以及统计分析等方面,探讨GWAS的研究现状以及存在的潜在问题,并展望GWAS今后的发展方向。展开更多
基金supported by the National Basic Research Program (973) of China (No. 2004CB117306)the Hi-Tech Research and Devel-opment Program (863) of China (No. 2006AA10A102)
文摘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.
基金the National Basic Research Program of China （2006CB 101700） the National Natural Science Foundation of China （30370758）+1 种基金 Program for New Century Excellent Talents in University, Ministry of Education of China （NCET-05-0502） the Natural Science Foundation of Jiangsu Province of China to Xu Chenwu （BK2006066）.
文摘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.
基金supported by the National Basic Research Program of China (2011CB109306 and 2009CB118404)the Program of Introducing Talents of Discipline to Universities of China ("111" Project, B06014)Research Programs (CNTC-D2011100, CNTC-[2012]146, NY-[2011]3047, QKHRZ [2013] 02)
文摘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.
基金the National Basic Research Program of China （2006CB 101700） Program for New Century Excellent Talents in University, Ministry of Education of China （NCET-05-0502） the Natural Science Foundation of Jiangsu Province （BK2006066）
文摘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-
基金Hendrech and Eiran Gotwert FundWellcome, Grant/Award Number: 085906/Z/08/Z, 075491/Z/04 and 090532/Z/09/Z+6 种基金Tel-Aviv UniversityIsraeli Science foundation, Grant/Award Number: 429/09, 961/15 and 1085/18Binational Science Foundation, Grant/Award Number: 2015077German Israeli Science Foundation, Grant/Award Number: I-63-410.20-2017Israeli Cancer Research FundCancer Research Counsel-UK Cancer Biology Research Center
文摘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.
基金supported by the National Natural Science Foundation of China(81171880)the National Basic Research Program of China(2011CB51001 to S.Huang)the GeNeSys Consortium(to O.Goldmann and E.Medina
文摘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.
基金supported by the National Natural Science Foundation of China(32370723,32000410)。
文摘Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits,the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge.This study introduces the dual-extraction modeling(DEM)approach,a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets,enabling the prediction of complex trait phenotypes.Through comprehensive benchmarking experiments,we demonstrate the efficacy of DEM in classification and regression prediction of complex traits.DEM consistently exhibits superior accuracy,robustness,generalizability,and flexibility.Notably,we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number,underscoring its commendable interpretability.In addition,we have developed user-friendly software to facilitate seamless utilization of DEM’s functions.In summary,this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes,confirming its potential as a valuable tool for exploring the genetic basis of complex traits.
基金Funding XS was in receipt of a National Natural Science Foundation of China(NSFC)grant(No.12171495)a Natural Science Foundation of Guangdong Province grant(No.2114050001435)+3 种基金a National Key Research and Development Program grant(No.2022YFF1202105)Swedish Research Council(Vetenskapsraet)grants(No.2017-02543&No.2022-01309)supported by the Swedish Research Council grant(No.2017-02543)XS The Swedish National Infrastructure for Computing(SNIC)utilized was partially funded by the Swedish Research Council through grant agreement No.2018-05973.
文摘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.
基金supported by the National Basic Research Program of China(2011CB109306and2010CB126006)the National Special Program for Breeding New Transgenic Variety(2009ZX08009-004B)the CNTC(110200701023)and the YNTC(08A05)
文摘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.
基金supported by the National Basic Research Program of China(2011CB109306)the National High Technology Research and Development Program of China(2009ZX08009-004B,2011AA10A102)+2 种基金the CNTC(110200701023)the YNTC(08A05)the earmearked fund for Modern Agro-industry Technology Reasearch System(CARS-18-05)
文摘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.
基金supported by the National Basic Research Program of China(2011CB100106)the National Natural Science Foundation of China(30971846and31171187)+2 种基金the Vital Project of Natural Science of Universities in Jiangsu Province(09KJA210002) to C.Xuthe National Natural Science Foundation of China(31100882) to Z.TangNational Natural Science Foundation of China(31000539) to J.Xiao
文摘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.
文摘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.
基金supported by research grants from National Natural Science Foundation of China (Nos. 31630042 and 91731302)
文摘Understanding how gene expression is translated to phenotype is central to modern molecular biology,and the success is contingent on the intrinsic tractability of the specific traits under examination.However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable.Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate(S_T)by gauging the number(N) of expression states that underlie the same trait abnormality, with large S_T corresponding to large N. By analyzing over 200 yeast morphological traits, we show that S_T predicts the tractability of an expression-trait relationship. We further show that S_T is ultimately determined by natural selection, which builds co-regulated gene modules to minimize possible expression states.
文摘全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,随着人类基因组计划和基因组单倍体图谱计划的实施,人们已通过GWAS方法发现并鉴定了大量与人类性状或复杂性疾病关联的遗传变异,为进一步了解控制人类复杂性疾病发生的遗传特征提供了重要的线索。然而,由于造成复杂性疾病/性状的因素较多,而且GWAS研究系统较为复杂,因此目前GWAS本身亦存在诸多的问题。本文将从研究方式、研究对象、遗传标记,以及统计分析等方面,探讨GWAS的研究现状以及存在的潜在问题,并展望GWAS今后的发展方向。
