scholarly journals Funmap2: an R package for QTL mapping using longitudinal phenotypes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7008
Author(s):  
Nating Wang ◽  
Tinyi Chu ◽  
Jiangtao Luo ◽  
Rongling Wu ◽  
Zhong Wang
Keyword(s):  
Qtl Mapping ◽  
Covariance Matrix ◽  
Complex Traits ◽  
Genetic Architecture ◽  
R Package ◽  
Information Criteria ◽  
Functional Mapping ◽  
Biological Knowledge ◽  
Phenotypic Traits ◽  
Trait Locus

Quantitative trait locus (QTL) mapping has been used as a powerful tool for inferring the complexity of the genetic architecture that underlies phenotypic traits. This approach has shown its unique power to map the developmental genetic architecture of complex traits by implementing longitudinal data analysis. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates prior biological knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describe the genetic effects and the covariance matrix of the trait of interest. Funmap2 chooses the type of longitudinal curve and covariance matrix automatically using information criteria. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

scholarly journals Funmap2: an R package for QTL mapping using longitudinal phenotypes

2017 ◽  
Author(s):  
Nating Wang ◽  
Tinyi Chu ◽  
Jiangtao Luo ◽  
Rongling Wu ◽  
Zhong Wang
Keyword(s):  
Qtl Mapping ◽  
Statistical Model ◽  
Prior Knowledge ◽  
Covariance Matrix ◽  
Genetic Architecture ◽  
Information Criterion ◽  
R Package ◽  
Genetic Effects ◽  
Functional Mapping ◽  
Phenotypic Traits

AbstractQTL mapping is a powerful tool to infer the complexity of the genetic architecture underlying phenotypic traits, and has been extended to include longitudinal traits measured at multiple temporal/spatial points. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates biological prior knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describes the genetic effects, and the covariance matrix of the trait of interest. Funmap2 may automatically choose the type of longitudinal curve and covariance matrix by information criterion. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

scholarly journals QTL Analysis for Downy Mildew Resistance in Cucumber Inbred Line PI 197088

Plant Disease ◽  
2018 ◽  
Vol 102 (7) ◽  
pp. 1240-1245 ◽  
Author(s):  
Lixia Li ◽  
Huiqiang He ◽  
Zhirong Zou ◽  
Yuhong Li
Keyword(s):  
Qtl Mapping ◽  
Downy Mildew ◽  
Genetic Architecture ◽  
Plant Introduction ◽  
Minor Effect ◽  
Phenotypic Variance ◽  
Chromosome 4 ◽  
Phenotypic Data ◽  
Trait Locus ◽  
Simple Sequence

Downy mildew (DM), caused by Pseudoperonospora cubensis, is one of the major foliar diseases prevailing in cucumber-growing areas. The mechanism of DM resistance in cucumber, particularly the plant introduction (PI) 197088 from India, is presently unclear. Quantitative trait locus (QTL) mapping is an efficient approach to studying DM resistance genes in cucumber. In this study, we performed QTL mapping for DM resistance in PI 197088 with 183 F2-derived F3 (F2:3) families from the cross between PI 197088 (DM resistant) and Changchunmici (DM susceptible). A linkage map was constructed using 141 simple sequence repeat markers. Phenotypic data were collected from seven independent experiments. In total, five QTL were detected on chromosomes 1, 3, 4, and 5 with DM resistance contributed by PI 197088. The QTL on chromosome 4, dm4.1, was reproducibly detected in all indoor experiments, which could explain 27% of the phenotypic variance detected. Additionally, dm1.1 and dm5.2 showed moderate effects, while dm3.1 and dm5.1 were minor-effect QTL. This study revealed the unique genetic architecture of DM resistance in PI 197088, which may provide important guidance for efficient use in cucumber breeding for DM resistance.

scholarly journals Explaining the heritability of an ecologically significant trait in terms of individual quantitative trait loci

2011 ◽  
Vol 7 (6) ◽  
pp. 896-898 ◽  
Author(s):  
Alison G. Scoville ◽  
Young Wha Lee ◽  
John H. Willis ◽  
John K. Kelly
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Natural Populations ◽  
Population Based ◽  
Population Variation ◽  
Trait Locus ◽  
Genomic Regions

Most natural populations display substantial genetic variation in behaviour, morphology, physiology, life history and the susceptibility to disease. A major challenge is to determine the contributions of individual loci to variation in complex traits. Quantitative trait locus (QTL) mapping has identified genomic regions affecting ecologically significant traits of many species. In nearly all cases, however, the importance of these QTLs to population variation remains unclear. In this paper, we apply a novel experimental method to parse the genetic variance of floral traits of the annual plant Mimulus guttatus into contributions of individual QTLs. We first use QTL-mapping to identify nine loci and then conduct a population-based breeding experiment to estimate V Q , the genetic variance attributable to each QTL. We find that three QTLs with moderate effects explain up to one-third of the genetic variance in the natural population. Variation at these loci is probably maintained by some form of balancing selection. Notably, the largest effect QTLs were relatively minor in their contribution to heritability.

scholarly journals Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

2021 ◽  
Author(s):  
Alex N. Nguyen Ba ◽  
Katherine R. Lawrence ◽  
Artur Rego-Costa ◽  
Shreyas Gopalakrishnan ◽  
Daniel Temko ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Trait Locus

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

scholarly journals QTL mapping in outbred populations: successes and challenges

2014 ◽  
Vol 46 (3) ◽  
pp. 81-90 ◽  
Author(s):  
Leah C. Solberg Woods
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Complex Traits ◽  
Full Genome Sequence ◽  
Backcross Population ◽  
Animal Populations ◽  
Resolution Mapping ◽  
Trait Locus ◽  
Genomic Regions ◽  
Outbred Populations

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

scholarly journals Deciphering Genetic Architecture of Adventitious Root and Related Shoot Traits in Populus Using QTL Mapping and RNA-Seq Data

2019 ◽  
Vol 20 (24) ◽  
pp. 6114 ◽  
Author(s):  
Pei Sun ◽  
Huixia Jia ◽  
Yahong Zhang ◽  
Jianbo Li ◽  
Mengzhu Lu ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Adventitious Root ◽  
Adventitious Roots ◽  
Genetic Architecture ◽  
Populus Deltoides ◽  
Transmembrane Domain ◽  
Root Traits ◽  
Rna Seq ◽  
Correlation Relationship ◽  
Trait Locus

Understanding the genetic architecture of adventitious root and related shoot traits will facilitate the cultivation of superior genotypes. In this study, we measured 12 adventitious root and related shoot traits of 434 F1 genotypes originating from Populus deltoides ‘Danhong’ × Populus simonii ‘Tongliao1’ and conducted an integrative analysis of quantitative trait locus (QTL) mapping and RNA-Seq data to dissect their genetic architecture and regulatory genes. Extensive segregation, high repeatability, and significant correlation relationship were detected for the investigated traits. A total of 150 QTLs were associated with adventitious root traits, explaining 3.1–6.1% of phenotypic variation (PVE); while 83 QTLs were associated with shoot traits, explaining 3.1–19.8% of PVE. Twenty-five QTL clusters and 40 QTL hotspots were identified for the investigated traits. Ten QTL clusters were overlapped in both adventitious root traits and related shoot traits. Transcriptome analysis identified 10,172 differentially expressed genes (DEGs) among two parents, three fine rooting and three poor-rooting genotypes, 143 of which were physically located within the QTL intervals. K-means cluster and weighted gene co-expression network analysis showed that PtAAAP19 (Potri.004G111400) encoding amino acid transport protein was tightly associated with adventitious roots and highly expressed in fine-rooting genotypes. Compare with ‘Danhong’, 153 bp deletion in the coding sequence of PtAAAP19 in ‘Tongliao1’ gave rise to lack one transmembrane domain, which might cause the variation of adventitious roots. Taken together, this study deciphered the genetic basis of adventitious root and related shoot traits and provided potential function genes for genetic improvement of poplar breeding.

scholarly journals Unravelling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus)

2021 ◽  
Author(s):  
Noemie Valenza-Troubat ◽  
Sara Montanari ◽  
Peter Ritchie ◽  
Maren Wellenreuther
Keyword(s):  
Qtl Mapping ◽  
Complex Traits ◽  
Genetic Basis ◽  
Growth Traits ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Powerful Approach ◽  
Trait Locus

AbstractGrowth directly influences production rate and therefore is one of the most important and well-studied trait in animal breeding. However, understanding the genetic basis of growth has been hindered by its typically complex polygenic architecture. Here, we performed quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) for 10 growth traits that were observed over two years in 1,100 F1 captive-bred trevally (Pseudocaranx georgianus). We constructed the first high-density linkage map for trevally, which included 19,861 single nucleotide polymorphism (SNP) markers, and discovered eight QTLs for height, length and weight on linkage groups 3, 14 and 18. Using GWAS, we further identified 113 SNP-trait associations, uncovering 10 genetic hot spots involved in growth. Two of the markers found in the GWAS co-located with the QTLs previously mentioned, demonstrating that combining QTL mapping and GWAS represents a powerful approach for the identification and validation of loci controlling complex traits. This is the first study of its kind for trevally. Our findings provide important insights into the genetic architecture of growth in this species and supply a basis for fine mapping QTLs, marker-assisted selection, and further detailed functional analysis of the genes underlying growth in trevally.

scholarly journals The Evolution of Larger Size in High Altitude Drosophila melanogaster has a Polymorphic Genetic Architecture

2021 ◽  
Author(s):  
Quentin D Sprengelmeyer ◽  
Justin B Lack ◽  
Dylan T Braun ◽  
Matthew J Monette ◽  
John E. Pool
Keyword(s):  
Drosophila Melanogaster ◽  
Qtl Mapping ◽  
Genetic Architecture ◽  
Natural Populations ◽  
Enrichment Analysis ◽  
Thorax Length ◽  
Adaptive Trait ◽  
Polygenic Adaptation ◽  
Trait Locus ◽  
Population Genetic Analyses

Important uncertainties persist regarding the genetic architecture of adaptive trait evolution in natural populations, including the number of genetic variants involved, whether they are drawn from standing genetic variation, and whether directional selection drives them to complete fixation. Here, we take advantage of a unique natural population of Drosophila melanogaster from the Ethiopian highlands, which has evolved larger body size than any other known population of this species. We apply a bulk segregant quantitative trait locus (QTL) mapping approach to four unique crosses between highland Ethiopian and lowland Zambian populations for both thorax length and wing length. Results indicated a persistently variable genetic basis for these evolved traits (with largely distinct sets of QTLs for each cross), and at least a moderately polygenic architecture with relatively strong effects present. We complemented these mapping experiments with population genetic analyses of QTL regions and gene ontology enrichment analysis, generating strong hypotheses for specific genes and functional processes that may have contributed to these adaptive trait changes. Finally, we find that the genetic architectures our QTL mapping results for size traits mirror those from similar experiments on other recently-evolved traits in this species. Collectively, these studies suggest a recurring pattern of polygenic adaptation in this species, in which causative variants do not approach fixation and moderately strong effect loci are present.

scholarly journals Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Genetics ◽  
2022 ◽  
Author(s):  
Stuart J Macdonald ◽  
Kristen M Cloud-Richardson ◽  
Dylan J Sims-West ◽  
Anthony D Long
Keyword(s):  
Qtl Mapping ◽  
Complex Traits ◽  
Recombinant Inbred Lines ◽  
Attractive Alternative ◽  
Trait Variation ◽  
Large Panels ◽  
Mapping Strategy ◽  
Trait Locus ◽  
Genomic Regions ◽  
And Control

Abstract Despite the value of Recombinant Inbred Lines (RILs) for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to RILs for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here we describe such an extreme quantitative trait locus, or X-QTL, mapping strategy that builds on an existing multiparental population, the DSPR (Drosophila Synthetic Population Resource), and involves phenotyping and genotyping a population derived by mixing hundreds of DSPR RILs. Simulations demonstrate that challenging, yet experimentally tractable X-QTL designs ( > =4 replicates, > =5000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional RIL-based QTL mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated X-QTL experiment that identifies 7 QTL for caffeine resistance. Two mapped X-QTL factors replicate loci previously identified in RILs, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping X-QTL design has considerable advantages.

scholarly journals The Evolution of Larger Size in High Altitude Drosophila melanogaster has a Variable Genetic Architecture

2022 ◽  
Author(s):  
Quentin D Sprengelmeyer ◽  
Justin B Lack ◽  
Dylan T Braun ◽  
Matthew J Monette ◽  
John E Pool
Keyword(s):  
Drosophila Melanogaster ◽  
Qtl Mapping ◽  
Genetic Architecture ◽  
Natural Populations ◽  
Enrichment Analysis ◽  
Thorax Length ◽  
Adaptive Trait ◽  
Polygenic Adaptation ◽  
Trait Locus ◽  
Population Genetic Analyses

Abstract Important uncertainties persist regarding the genetic architecture of adaptive trait evolution in natural populations, including the number of genetic variants involved, whether they are drawn from standing genetic variation, and whether directional selection drives them to complete fixation. Here, we take advantage of a unique natural population of Drosophila melanogaster from the Ethiopian highlands, which has evolved larger body size than any other known population of this species. We apply a bulk segregant quantitative trait locus (QTL) mapping approach to four unique crosses between highland Ethiopian and lowland Zambian populations for both thorax length and wing length. Results indicated a persistently variable genetic basis for these evolved traits (with largely distinct sets of QTLs for each cross), and at least a moderately polygenic architecture with relatively strong effects present. We complemented these mapping experiments with population genetic analyses of QTL regions and gene ontology enrichment analysis, generating strong hypotheses for specific genes and functional processes that may have contributed to these adaptive trait changes. Finally, we find that the genetic architectures our QTL mapping results for size traits mirror those from similar experiments on other recently-evolved traits in this species. Collectively, these studies suggest a recurring pattern of polygenic adaptation in this species, in which causative variants do not approach fixation and moderately strong effect loci are present.

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