Quantitative Trait Loci (QTL) Study Identifies Novel Genomic Regions Associated to Chiari-Like Malformation in Griffon Bruxellois Dogs

Abstract A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.

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Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

10.1101/188235 ◽

2017 ◽

Cited By ~ 1

Author(s):

Fanny Bonnafous ◽

Ghislain Fievet ◽

Nicolas Blanchet ◽

Marie-Claude Boniface ◽

Sébastien Carrère ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Flowering Time ◽

Genetic Control ◽

Quantitative Trait ◽

Complex Traits ◽

Association Studies ◽

Additive Models ◽

Genome Wide Association Studies ◽

Trait Loci ◽

Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

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Identification of main-effect quantitative trait loci (QTLs) for low-temperature stress tolerance germination- and early seedling vigor-related traits in rice (Oryza sativa L.)

Molecular Breeding ◽

10.1007/s11032-019-1090-4 ◽

2020 ◽

Vol 40 (1) ◽

Cited By ~ 5

Author(s):

S. Najeeb ◽

J. Ali ◽

A. Mahender ◽

Y.L. Pang ◽

J. Zilhas ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Low Temperature ◽

Quantitative Trait ◽

Root Length ◽

Stress Index ◽

Multiple Traits ◽

Main Effect ◽

Trait Loci ◽

Early Seedling ◽

Genomic Regions

AbstractAn attempt was made in the current study to identify the main-effect and co-localized quantitative trait loci (QTLs) for germination and early seedling growth traits under low-temperature stress (LTS) conditions in rice. The plant material used in this study was an early backcross population of 230 introgression lines (ILs) in BCIF7 generation derived from the Weed Tolerant Rice-1 (WTR-1) (as the recipient) and Haoannong (HNG) (as the donor). Genetic analyses of LTS tolerance revealed a total of 27 main-effect quantitative trait loci (M-QTLs) mapped on 12 chromosomes. These QTLs explained more than 10% of phenotypic variance (PV), and average PV of 12.71% while employing 704 high-quality SNP markers. Of these 27 QTLs distributed on 12 chromosomes, 11 were associated with low-temperature germination (LTG), nine with low-temperature germination stress index (LTGS), five with root length stress index (RLSI), and two with biomass stress index (BMSI) QTLs, shoot length stress index (SLSI) and root length stress index (RLSI), seven with seed vigor index (SVI), and single QTL with root length (RL). Among them, five significant major QTLs (qLTG(I)1, qLTGS(I)1–2, qLTG(I)5, qLTGS(I)5, and qLTG(I)7) mapped on chromosomes 1, 5, and 7 were associated with LTG and LTGS traits and the PV explained ranged from 16 to 23.3%. The genomic regions of these QTLs were co-localized with two to six QTLs. Most of the QTLs were growth stage-specific and found to harbor QTLs governing multiple traits. Eight chromosomes had more than four QTLs and were clustered together and designated as promising LTS tolerance QTLs (qLTTs), as qLTT1, qLTT2, qLTT3, qLTT5, qLTT6, qLTT8, qLTT9, and qLTT11. A total of 16 putative candidate genes were identified in the major M-QTLs and co-localized QTL regions distributed on different chromosomes. Overall, these significant genomic regions of M-QTLs are responsible for multiple traits and this suggested that these could serve as the best predictors of LTS tolerance at germination and early seedling growth stages. Furthermore, it is necessary to fine-map these regions and to find functional markers for marker-assisted selection in rice breeding programs for cold tolerance.

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Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

Methods and Protocols ◽

10.3390/mps3020028 ◽

2020 ◽

Vol 3 (2) ◽

pp. 28 ◽

Cited By ~ 1

Author(s):

Frank M. You ◽

Sylvie Cloutier

Keyword(s):

Quantitative Trait Loci ◽

Candidate Genes ◽

Quantitative Trait ◽

Quantitative Traits ◽

Snp Markers ◽

Genetic Maps ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Trait Loci ◽

Genomic Regions

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits. To date, a total of 313 QTL for 31 quantitative traits have been reported in 14 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, the scaffold sequences, or the pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but the most used ones were simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). To uniquely map the SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules, methods with several scripts and database files were developed to locate PCR- and SNP-based markers onto the same reference, co-locate QTL, and scan genome-wide candidate genes. Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.

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Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment

Functional Plant Biology ◽

10.1071/fp09219 ◽

2010 ◽

Vol 37 (2) ◽

pp. 85 ◽

Cited By ~ 199

Author(s):

Richard A. Richards ◽

Greg J. Rebetzke ◽

Michelle Watt ◽

A. G. (Tony) Condon ◽

Wolfgang Spielmeyer ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Water Productivity ◽

Crop Improvement ◽

Phenotypic Selection ◽

Trait Loci ◽

Sampling Cost ◽

Major Bottleneck ◽

Improvement Programs ◽

Genomic Regions

Consistent gains in grain yield in dry environments have been made by empirical breeding although there is disturbing evidence that these gains may have slowed. There are few examples where an understanding of the physiology and the genetics of putative important drought-related traits has led to improved yields. Success will first depend on identifying the most important traits in the target regions. It will then depend on accurate and fast phenotyping, which, in turn, will lead to: (1) trait-based selection being immediately transferable into breeding operations and (2) being able to identify the underlying genes or the important genomic regions (quantitative trait loci), perhaps leading to efficient marker-based selection (MBS). Genetic complexity, extent of genotype × environment (G × E) interaction and sampling cost per line will determine value of phenotyping over MBS methods. Here, we review traits of importance in dry environments and review whether molecular or phenotypic selection methods are likely to be the most effective in crop improvement programs and where the main bottlenecks to selection are. We also consider whether selection for these traits should be made in dry environments or environments where there is no soil water limitation. The development of lines/populations for trait validation studies and for varietal development is also described. We firstly conclude that despite the spectacular improvements in molecular technologies, fast and accurate phenotyping remains the major bottleneck to enhancing yield gains in water-limited environments. Secondly, for most traits of importance in dry environments, selection is generally conducted most effectively in favourable moisture environments.

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Mapping of Genomic Regions (Quantitative Trait Loci) Controlling Production and Quality in Industrial Cultures of the Edible Basidiomycete Pleurotus ostreatus

Applied and Environmental Microbiology ◽

10.1128/aem.69.6.3617-3625.2003 ◽

2003 ◽

Vol 69 (6) ◽

pp. 3617-3625 ◽

Cited By ~ 27

Author(s):

Luis M. Larraya ◽

Mikel Alfonso ◽

Antonio G. Pisabarro ◽

LucÃ¯Â¿Â½a RamÃ¯Â¿Â½rez

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Pleurotus Ostreatus ◽

Production Traits ◽

Trial And Error ◽

Trait Loci ◽

Industrial Strains ◽

Culture Production ◽

Culture Process ◽

Genomic Regions

ABSTRACT Industrial production of the edible basidiomycete Pleurotus ostreatus (oyster mushroom) is based on a solid fermentation process in which a limited number of selected strains are used. Optimization of industrial mushroom production depends on improving the culture process and breeding new strains with higher yields and productivities. Traditionally, fungal breeding has been carried out by an empirical trial and error process. In this study, we used a different approach by mapping quantitative trait loci (QTLs) controlling culture production and quality within the framework of the genetic linkage map of P. ostreatus. Ten production traits and four quality traits were studied and mapped. The production QTLs identified explain nearly one-half of the production variation. More interestingly, a single QTL mapping to the highly polymorphic chromosome VII appears to be involved in control of all the productivity traits studied. Quality QTLs appear to be scattered across the genome and to have less effect on the variation of the corresponding traits. Moreover, some of the new hybrid strains constructed in the course of our experiments had production or quality values higher than those of the parents or other commercial strains. This approach opens the possibility of marker-assisted selection and breeding of new industrial strains of this fungus.

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ON THE POWER TO DETECT SNP/PHENOTYPE ASSOCIATION IN CANDIDATE QUANTITATIVE TRAIT LOCI GENOMIC REGIONS: A SIMULATION STUDY

Biocomputing 2003 ◽

10.1142/9789812776303_0045 ◽

2002 ◽

Author(s):

JOSEP M. COMERON ◽

MARTIN KREITMAN ◽

FRANCISCO M. DE LA VEGA

Keyword(s):

Quantitative Trait Loci ◽

Simulation Study ◽

Quantitative Trait ◽

Trait Loci ◽

Genomic Regions

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Genome-wide Association Study for Carcass Primal Cut Yields Using Single-step Bayesian Approach in Hanwoo Cattle

Frontiers in Genetics ◽

10.3389/fgene.2021.752424 ◽

2021 ◽

Vol 12 ◽

Author(s):

Masoumeh Naserkheil ◽

Hossein Mehrban ◽

Deukmin Lee ◽

Mi Na Park

Keyword(s):

Quantitative Trait Loci ◽

Candidate Genes ◽

Quantitative Trait ◽

Single Step ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Trait Loci ◽

Genomic Regions ◽

Hanwoo Cattle

The importance of meat and carcass quality is growing in beef cattle production to meet both producer and consumer demands. Primal cut yields, which reflect the body compositions of carcass, could determine the carcass grade and, consequently, command premium prices. Despite its importance, there have been few genome-wide association studies on these traits. This study aimed to identify genomic regions and putative candidate genes related to 10 primal cut traits, including tenderloin, sirloin, striploin, chuck, brisket, top round, bottom round, shank, flank, and rib in Hanwoo cattle using a single-step Bayesian regression (ssBR) approach. After genomic data quality control, 43,987 SNPs from 3,745 genotyped animals were available, of which 3,467 had phenotypic records for the analyzed traits. A total of 16 significant genomic regions (1-Mb window) were identified, of which five large-effect quantitative trait loci (QTLs) located on chromosomes 6 at 38–39 Mb, 11 at 21–22 Mb, 14 at 6–7 Mb and 26–27 Mb, and 19 at 26–27 Mb were associated with more than one trait, while the remaining 11 QTLs were trait-specific. These significant regions were harbored by 154 genes, among which TOX, FAM184B, SPP1, IBSP, PKD2, SDCBP, PIGY, LCORL, NCAPG, and ABCG2 were noteworthy. Enrichment analysis revealed biological processes and functional terms involved in growth and lipid metabolism, such as growth (GO:0040007), muscle structure development (GO:0061061), skeletal system development (GO:0001501), animal organ development (GO:0048513), lipid metabolic process (GO:0006629), response to lipid (GO:0033993), metabolic pathways (bta01100), focal adhesion (bta04510), ECM–receptor interaction (bta04512), fat digestion and absorption (bta04975), and Rap1 signaling pathway (bta04015) being the most significant for the carcass primal cut traits. Thus, identification of quantitative trait loci regions and plausible candidate genes will aid in a better understanding of the genetic and biological mechanisms regulating carcass primal cut yields.

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Chromosomes I and X Harbor Consistent Genetic Factors Associated with the Anthocyanin Variation in Potato

Agronomy ◽

10.3390/agronomy9070366 ◽

2019 ◽

Vol 9 (7) ◽

pp. 366 ◽

Cited By ~ 3

Author(s):

María-Angélica Parra-Galindo ◽

Clara Piñeros-Niño ◽

Johana Carolina Soto-Sedano ◽

Teresa Mosquera-Vasquez

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Genetic Factors ◽

Germplasm Collection ◽

Potato Tubers ◽

High Positive Correlation ◽

A Genome ◽

Trait Loci ◽

Nutritional Compounds ◽

Genomic Regions

Potatoes are an important staple food worldwide and are the third main source of antioxidants in the human diet. One of the most important antioxidant compounds in potatoes is the anthocyanin pigments. Some reports indicate a high positive correlation between color intensity, anthocyanins content, and antioxidant level in potato tubers. The variation in anthocyanins composition and content in potato tubers among diverse germplasm sources has important nutritional and health implications and constitutes an interesting trait for potato breeding programs focused on enhancing the anthocyanin and antioxidant contents of potato materials. We identified and quantified five anthocyanidins (delphinidin, cyanidin, petunidin, pelargonidin, and peonidin) on tubers from the Colombian germplasm collection of Solanum tuberosum L. Group Phureja. The phenotypic data were merged into a genome-wide association study in order to identify genomic regions associated with the nutritional compounds’ variation in potatoes. The association was conducted using a 7520 single nucleotide polymorphisms markers matrix. Seven quantitative trait loci were identified. Chromosomes I and X harbored the most stable quantitative trait loci (QTL). Three quantitative trait loci were identified close to previously reported genes involved in the regulation of anthocyanins in potato tubers. The genomic regions of these QTL reveal presumptive candidate genes as genetic factors that are the basis for a better understanding of the genetic architecture of the regulation of nutritional compounds in potatoes.

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A Whole-Genome Linkage Scan Suggests Several Genomic Regions Potentially Containing Quantitative Trait Loci for Osteoporosis

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2002-020474 ◽

2002 ◽

Vol 87 (11) ◽

pp. 5151-5159 ◽

Cited By ~ 105

Author(s):

Hong-Wen Deng ◽

Fu-Hua Xu ◽

Qing-Yang Huang ◽

Hui Shen ◽

Hongyi Deng ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Whole Genome ◽

Mineral Density ◽

Lod Score ◽

Multipoint Analysis ◽

Sibling Pairs ◽

Point Analysis ◽

Trait Loci ◽

Genomic Regions

Abstract Osteoporosis is an important health problem, particularly in the elderly women. Bone mineral density (BMD) is a major determinant of osteoporosis. For a sample of 53 pedigrees that contain 1249 sibling pairs, 1098 grandparent-grandchildren pairs, and 2589 first cousin pairs, we performed a whole- genome linkage scan using 380 microsatellite markers to identify genomic regions that may contain quantitative trait loci (QTL) of BMD. Each pedigree was ascertained through a proband with BMD values belonging to the bottom 10% of the population. We conducted two-point and multipoint linkage analyses. Several potentially important genomic regions were suggested. For example, the genomic region near the marker D10S1651 may contain a QTL for hip BMD variation (with two-point analysis LOD score of 1.97 and multipoint analysis LOD score of 2.29). The genomic regions near the markers D4S413 and D12S1723 may contain QTLs for spine BMD variation (with two-point analysis LOD score of 2.12 and 2.17 and multipoint analysis LOD score of 3.08 and 2.96, respectively). The genomic regions identified in this and some earlier reports are compared for exploration in extension studies with larger samples and/or denser markers for confirmation and fine mapping to eventually identify major functional genes involved in osteoporosis.

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