Multiple alleles for tuber shape in diploid potato detected by qualitative and quantitative genetic analysis using RFLPs.

Abstract Tuber shape in potato is commonly regarded as displaying continuous variation, yet at the diploid level phenotypes can be discerned visually, having round or long tubers. Inheritance of qualitative tuber shape can be explained by a single locus Ro, round being dominant to long. With restriction fragment length polymorphisms (RFLPs) the Ro locus was mapped on chromosome 10. Tuber shape was also studied as a quantitative trait, using the length/width ratio as trait value. The estimated broad sense heritability was h2 = 0.80. The morphologically mapped Ro locus explained 75% of the genetic variation, indicating the presence of a major quantitative trait locus (QTL) at the Ro locus and minor genetic factors. RFLP alleles linked with Ro alleles were used to divide the progeny into four genotypic classes: RofemaleRomale:Rofemalero:roRomale:roro = 1:1:1:1. The recessive ro allele is identical by descent in both parents. The significantly different effects (P = 0.0157) of the non-identical alleles Rofemale and Romale provided evidence for multiallelism at the Ro locus. Linkage mapping of the Ro locus was compared with QTL mapping. Only those markers which are polymorphic in both parents allow accurate QTL mapping when genetic factors segregate from both parents. This finding applies to QTL mapping in all outbreeders without homozygous inbred strains.

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Quantitative trait locus mapping of airway responsiveness to chromosomes 6 and 7 in inbred mice

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.1999.277.6.l1118 ◽

1999 ◽

Vol 277 (6) ◽

pp. L1118-L1123 ◽

Cited By ~ 13

Author(s):

G. T. De Sanctis ◽

J. B. Singer ◽

A. Jiao ◽

C. N. Yandava ◽

Y. H. Lee ◽

...

Keyword(s):

Quantitative Trait Locus ◽

Qtl Mapping ◽

Quantitative Trait ◽

Genetic Factors ◽

Inbred Mice ◽

Airway Responsiveness ◽

Chromosome 17 ◽

Chromosome 6 ◽

Trait Locus ◽

Locus Mapping

Quantitative trait locus (QTL) mapping was used to identify chromosomal regions contributing to airway hyperresponsiveness in mice. Airway responsiveness to methacholine was measured in A/J and C3H/HeJ parental strains as well as in progeny derived from crosses between these strains. QTL mapping of backcross [(A/J × C3H/HeJ) × C3H/HeJ] progeny ( n = 137–227 informative mice for markers tested) revealed two significant linkages to loci on chromosomes 6 and 7. The QTL on chromosome 6 confirms the previous report by others of a linkage in this region in the same genetic backgrounds; the second QTL, on chromosome 7, represents a novel locus. In addition, we obtained suggestive evidence for linkage (logarithm of odds ratio = 1.7) on chromosome 17, which lies in the same region previously identified in a cross between A/J and C57BL/6J mice. Airway responsiveness in a cross between A/J and C3H/HeJ mice is under the control of at least two major genetic loci, with evidence for a third locus that has been previously implicated in an A/J and C57BL/6J cross; this indicates that multiple genetic factors control the expression of this phenotype.

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Quantitative trait loci associated with maximal exercise endurance in mice

Journal of Applied Physiology ◽

10.1152/japplphysiol.01328.2006 ◽

2007 ◽

Vol 103 (1) ◽

pp. 105-110 ◽

Cited By ~ 24

Author(s):

J. Timothy Lightfoot ◽

Michael J. Turner ◽

Amy Kleinfehn Knab ◽

Anne E. Jedlicka ◽

Tomohiro Oshimura ◽

...

Keyword(s):

Quantitative Trait ◽

Genetic Factors ◽

Maximal Exercise ◽

Inbred Mouse ◽

Inbred Strains ◽

Significant Quantitative Trait Locus ◽

Chromosome 8 ◽

Entire Cohort ◽

Exercise Endurance ◽

Trait Locus

The role of genetics in the determination of maximal exercise endurance is unclear. Six- to nine-week-old F2 mice ( n = 99; 60 female, 39 male), derived from an intercross of two inbred strains that had previously been phenotyped as having high maximal exercise endurance (Balb/cJ) and low maximal exercise endurance (DBA/2J), were treadmill tested to estimate exercise endurance. Selective genotyping of the F2 cohort ( n = 12 high exercise endurance; n = 12 low exercise endurance) identified a significant quantitative trait locus (QTL) on chromosome X (53.7 cM, DXMit121) in the entire cohort and a suggestive QTL on chromosome 8 (36.1 cM, D8Mit359) in the female mice. Fine mapping with the entire F2 cohort and additional informative markers confirmed and narrowed the QTLs. The chromosome 8 QTL ( EE8 F) is homologous with two suggestive human QTLs and one significant rat QTL previously linked with exercise endurance. No effect of sex ( P = 0.33) or body weight ( P = 0.79) on exercise endurance was found in the F2 cohort. These data indicate that genetic factors in distinct chromosomal regions may affect maximal exercise endurance in the inbred mouse. Whereas multiple genes are located in the identified QTL that could functionally affect exercise endurance, this study serves as a foundation for further investigations delineating the identity of genetic factors influencing maximum exercise endurance.

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Consequences of Recombination Rate Variation on Quantitative Trait Locus Mapping Studies: Simulations Based on the Drosophila melanogaster Genome

Genetics ◽

10.1093/genetics/159.2.581 ◽

2001 ◽

Vol 159 (2) ◽

pp. 581-588

Author(s):

Mohamed A F Noor ◽

Aimee L Cunningham ◽

John C Larkin

Keyword(s):

Quantitative Trait Locus ◽

Drosophila Melanogaster ◽

Qtl Mapping ◽

Quantitative Trait ◽

Recombination Rate ◽

Genome Project ◽

Gene Density ◽

Rate Variation ◽

Trait Locus ◽

Recombination Rate Variation

Abstract We examine the effect of variation in gene density per centimorgan on quantitative trait locus (QTL) mapping studies using data from the Drosophila melanogaster genome project and documented regional rates of recombination. There is tremendous variation in gene density per centimorgan across this genome, and we observe that this variation can cause systematic biases in QTL mapping studies. Specifically, in our simulated mapping experiments of 50 equal-effect QTL distributed randomly across the physical genome, very strong QTL are consistently detected near the centromeres of the two major autosomes, and few or no QTL are often detected on the X chromosome. This pattern persisted with varying heritability, marker density, QTL effect sizes, and transgressive segregation. Our results are consistent with empirical data collected from QTL mapping studies of this species and its close relatives, and they explain the “small X-effect” that has been documented in genetic studies of sexual isolation in the D. melanogaster group. Because of the biases resulting from recombination rate variation, results of QTL mapping studies should be taken as hypotheses to be tested by additional genetic methods, particularly in species for which detailed genetic and physical genome maps are not available.

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Identification of RAPD markers for percent hull in oat

Genome ◽

10.1139/g97-813 ◽

1997 ◽

Vol 40 (6) ◽

pp. 873-878 ◽

Cited By ~ 12

Author(s):

P. S. Ronald ◽

G. A. Penner ◽

P. D. Brown ◽

A. Brûlé-Babel

Keyword(s):

Quantitative Trait Locus ◽

Quantitative Trait ◽

Genetic Factors ◽

Bulked Segregant Analysis ◽

Multiple Time ◽

Test Statistics ◽

Marker Loci ◽

Correct Determination ◽

Trait Locus

Percent hull is an important physical parameter of oat grain quality, but it is affected by environment. Multiple time-consuming evaluations are required to obtain a correct determination of phenotype. The application of marker-assisted selection for the genes involved would greatly simplify the identification of desirable oat genotypes. Bulked segregant analysis, with selected progeny lines derived from a cross between Cascade and AC Marie (30 and 23% hull, respectively), was used to identify randomly amplified polymorphic DNA markers linked to genetic factors controlling primary kernel hull percentage in oat. Twelve polymorphisms, identified between bulks, were tested for linkage to genetic factors controlling hull percentage by genotyping 80 randomly selected F2-derived F8 lines from the progeny population. Three markers showed significant test statistics for quantitative trait locus effects, when tested with primary kernel percent hull data from two environments. Together, the unlinked marker loci OPC13800, OPD20600, and OPK71300 explained approximately 41% of the genetic variance in primary kernel percent hull, after accounting for the main effect of environment.Key words: Avena sativa, hull percentage, bulked segregant analysis, quantitative trait locus.

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A method for computing identity by descent probabilities and quantitative trait loci mapping with dominant (AFLP) markers

Genetics Research ◽

10.1017/s0016672302005645 ◽

2002 ◽

Vol 79 (3) ◽

pp. 247-258 ◽

Cited By ~ 3

Author(s):

MIGUEL PÉREZ-ENCISO ◽

ODILE ROUSSOT

Keyword(s):

Quantitative Trait Loci ◽

Qtl Mapping ◽

Quantitative Trait ◽

Cost Effective ◽

Significant Loss ◽

Outbred Population ◽

Identity By Descent ◽

Fluorescent Band ◽

Length Polymorphisms ◽

Trait Loci

Amplified fragment length polymorphisms (AFLPs) are a widely used marker system: the technique is very cost-effective, easy and rapid, and reproducibly generates hundreds of markers. Unfortunately, AFLP alleles are typically scored as the presence or absence of a band and, thus, heterozygous and dominant homozygous genotypes cannot be distinguished. This results in a significant loss of information, especially as regards mapping of quantitative trait loci (QTLs). We present a Monte Carlo Markov Chain method that allows us to compute the identity by descent probabilities (IBD) in a general pedigree whose individuals have been typed for dominant markers. The method allows us to include the information provided by the fluorescent band intensities of the markers, the rationale being that homozygous individuals have on average higher band intensities than heterozygous individuals, as well as information from linked markers in each individual and its relatives. Once IBD probabilities are obtained, they can be combined into the QTL mapping strategy of choice. We illustrate the method with two simulated populations: an outbred population consisting of full sib families, and an F2 cross between inbred lines. Two marker spacings were considered, 5 or 20 cM, in the outbred population. There was almost no difference, for the practical purpose of QTL estimation, between AFLPs and biallelic codominant markers when the band density is taken into account, especially at the 5 cM spacing. The performance of AFLPs every 5 cM was also comparable to that of highly polymorphic markers (microsatellites) spaced every 20 cM. In economic terms, QTL mapping with a dense map of AFLPs is clearly better than microsatellite QTL mapping and little is lost in terms of accuracy of position. Nevertheless, at low marker densities, AFLPs or other biallelic markers result in very inaccurate estimates of QTL position.

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

Biology Letters ◽

10.1098/rsbl.2011.0409 ◽

2011 ◽

Vol 7 (6) ◽

pp. 896-898 ◽

Cited By ~ 13

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.

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The contribution of dominance to the understanding of quantitative genetic variation

Genetics Research ◽

10.1017/s0016672310000649 ◽

2011 ◽

Vol 93 (2) ◽

pp. 139-154 ◽

Cited By ~ 29

Author(s):

ROBIN WELLMANN ◽

JÖRN BENNEWITZ

Keyword(s):

Qtl Mapping ◽

Inbreeding Depression ◽

Quantitative Trait ◽

Breeding Value ◽

Additive Effects ◽

Additive Variance ◽

Dominance Variance ◽

True Value ◽

Value Estimation ◽

Trait Locus

SummaryKnowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

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

10.1101/2021.09.08.459513 ◽

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.

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High-Density Genetic Linkage Map Construction and White Rot Resistance Quantitative Trait Loci Mapping for Genus Vitis Based on Restriction Site-Associated DNA Sequencing

Phytopathology ◽

10.1094/phyto-12-19-0480-r ◽

2021 ◽

pp. PHYTO-12-19-048

Author(s):

Kai Su ◽

Yinshan Guo ◽

Weihao Zhong ◽

Hong Lin ◽

Zhendong Liu ◽

...

Keyword(s):

Qtl Mapping ◽

Dna Sequencing ◽

Quantitative Trait ◽

Restriction Site ◽

Genetic Distances ◽

Snp Markers ◽

High Density ◽

White Rot ◽

Phenotypic Variance ◽

Trait Locus

Grape white rot (Coniothyrium diplodiella) is a major fungal disease affecting grape yield and quality. Quantitative trait locus (QTL) analysis is an important method for studying important horticultural traits of grapevine. This study was conducted to construct a high-density map and conduct QTL mapping for grapevine white rot resistance. A mapping population with 177 genotypes was developed from interspecific hybridization of a white rot-resistant cultivar (Vitis vinifera × V. labrusca ‘Zhuosexiang’) and white rot-susceptible cultivar (V. vinifera ‘Victoria’). Single-nucleotide polymorphism (SNP) markers were developed by restriction site-associated DNA sequencing. The female, male, and integrated maps contained 2,501, 4,110, and 6,249 SNP markers with average genetic distances of adjacent markers of 1.25, 0.77, and 0.50 cM, respectively. QTL mapping was conducted based on white rot resistance identification of 177 individuals in July and August of 2017 and 2018. Notably, one stable QTL related to white rot resistance was detected and located on linkage group LG14. The phenotypic variance ranged from 12.93 to 13.43%. An SNP marker (chr14_3929380), which cosegregated with white rot resistance, was discovered and shows potential for use in marker-assisted selection to generate new grapevine cultivars with resistance to white rot.

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