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 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 Robust Methods for Analysing Quantitative Trait Loci

2021 ◽  
Author(s):  
◽  
Nuovella Williams
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
New Technology ◽  
Interval Mapping ◽  
Likelihood Methods ◽  
Tissue Samples ◽  
Phenotype Data ◽  
Trait Locus ◽  
Information Matrices

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

scholarly journals Robust Methods for Analysing Quantitative Trait Loci

2021 ◽  
Author(s):  
◽  
Nuovella Williams
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
New Technology ◽  
Interval Mapping ◽  
Likelihood Methods ◽  
Tissue Samples ◽  
Phenotype Data ◽  
Trait Locus ◽  
Information Matrices

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

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

2021 ◽  
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

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 a selection intensity of 5-10%) 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 hairy: A Quantitative Trait Locus for Drosophila Sensory Bristle Number

Genetics ◽  
2002 ◽  
Vol 162 (1) ◽  
pp. 155-164 ◽  
Author(s):  
Charles Robin ◽  
Richard F Lyman ◽  
Anthony D Long ◽  
Charles H Langley ◽  
Trudy F C Mackay
Keyword(s):  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Variance ◽  
Transcription Start ◽  
Single Population ◽  
Conditional Effects ◽  
Bristle Number ◽  
Trait Locus ◽  
Flanking Regions

AbstractAdvances in medicine, agriculture, and an understanding of evolution depend on resolving the genetic architecture of quantitative traits, which is challenging since variation for complex traits is caused by multiple interacting quantitative trait loci (QTL) with small and conditional effects. Here, we show that the key developmental gene, hairy (h), is a QTL for Drosophila sternopleural bristle number, a model quantitative trait. Near-isoallelic lines (NIL) for the h gene region exhibited significant variation in bristle number and failed to complement a hairy mutation. Sequencing 10 h alleles from a single population revealed 330 polymorphic sites in ∼10 kb. Genotypes for 25 of these and 14 additional sites in the flanking regions were determined for the 57 NIL and associated with variation in bristle number in four genetic backgrounds. A highly significant association was found for a complicated insertion/deletion polymorphism upstream of the transcription start site. This polymorphism, present in 17.5% of the h alleles, was associated with an increase of 0.5 bristle and accounted for 31% of the genetic variance in bristle number in the NIL.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

Genetic dissection of partial resistance to race 6 of Venturia inaequalis in apple

Genome ◽  
2003 ◽  
Vol 46 (2) ◽  
pp. 224-234 ◽  
Author(s):  
C E Durel ◽  
L Parisi ◽  
F Laurens ◽  
W E Van de Weg ◽  
R Liebhard ◽  
...  
Keyword(s):  
Resistance Gene ◽  
Malus Domestica ◽  
Quantitative Trait ◽  
Partial Resistance ◽  
Venturia Inaequalis ◽  
Genetic Dissection ◽  
Resistance Breakdown ◽  
Trait Locus ◽  
New Races ◽  
Genomic Regions

Scab, caused by the fungus Venturia inaequalis, is one of the most important diseases of apple (Malus × domestica). The major resistance gene, Vf, has been widely used in apple breeding programs, but two new races of the fungus (races 6 and 7) are able to overcome this gene. A mapped F1 progeny derived from a cross between the cultivars Prima and Fiesta has been inoculated with two monoconidial strains of race 6. These strains originated from sporulating leaves of 'Prima' and a descendant of 'Prima' that were grown in an orchard in northern Germany. 'Prima' carries the Vf resistance gene, whereas 'Fiesta' lacks Vf. A large variation in resistance and (or) susceptibility was observed among the individuals of the progeny. Several quantitative trait loci (QTLs) for resistance were identified that mapped on four genomic regions. One of them was located in the very close vicinity of the Vf resistance gene on linkage group LG-1 of the 'Prima' genetic map. This QTL is isolate specific because it was only detected with one of the two isolates. Two out of the three other genomic regions were identified with both isolates (LG-11 and LG-17). On LG-11, a QTL effect was detected in both parents. The genetic dissection of this QTL indicated a favourable intra-locus interaction between some parental alleles.Key words: Malus × domestica, partial resistance, Venturia inaequalis, resistance breakdown, quantitative trait locus.

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