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QTL Alleles (qtl + allele)
Selected AbstractsA major QTL for resistance of rice to the parasitic plant Striga hermonthica is not dependent on genetic backgroundPEST MANAGEMENT SCIENCE (FORMERLY: PESTICIDE SCIENCE), Issue 5 2009Philip J Swarbrick Abstract BACKGROUND: The use of Striga -resistant germplasm is likely to be a cost-effective control strategy for preventing loss of yield owing to Striga. Previously, the authors identified quantitative trait loci (QTL) for resistance in rice to Striga hermonthica (Del.) Benth. in backcross inbred lines (BILs) derived from a cross between two cultivars Nipponbare and Kasalath. It is essential to validate QTL in different environments and/or genetic backgrounds to develop molecular markers linked to resistance QTL for use in marker-assisted selection (MAS) programmes. This study aimed to establish whether a large-effect Kasalath-derived resistance QTL allele on chromosome 4 of rice also conferred resistance in a different mapping population derived from a cross between Koshihikari and Kasalath, and to identify any further Striga resistance QTL. RESULTS: Three Striga resistance QTL were detected in Koshihikari,Kasalath BILs, two of which were derived from the Kasalath allele and one from the Koshihkari allele. The largest QTL (Kasalath allele) explained 16% of the variation in the mapping population and was located on chromosome 4. Comparison between these data and those of the authors' previous analysis revealed that the confidence intervals of the chromosome-4 QTL in the Nipponbare,Kasalath cross and the Kasalath,Koshihikari cross overlapped between 6.5 Mbp and 8 Mbp on the physical rice genome assembly. CONCLUSION: This study has both verified and narrowed down the position of a Striga resistance QTL of major effect, and demonstrated that it may be a tractable target for MAS. Copyright © 2009 Society of Chemical Industry [source] Power of Linkage Disequilibrium Mapping to Detect a Quantitative Trait Locus (QTL) in Selected Samples of Unrelated IndividualsANNALS OF HUMAN GENETICS, Issue 6 2003A. Tenesa Summary We considered a strategy to map quantitative trait loci (QTLs) using linkage disequilibrium (LD) when the QTL and marker locus were multiallelic. The strategy involved phenotyping a large number of unrelated individuals and genotyping only selected individuals from the two tails of the trait distribution. Power to detect trait-marker association was assessed as a function of the number of QTL and marker alleles. Two patterns of LD were used to study their influence on power. When the frequency of the QTL allele with the largest effect and that of the marker allele linked in coupling were equal, power was maximum. In this case, increasing the number of QTL alleles reduced the power. The maximum difference in power between the two LD patterns studied was ,30%. For low QTL heritabilities (h2QTL < 0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution. [source] Top down preselection using marker assisted estimates of breeding values in dairy cattleJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2004Jörn Bennewitz Summary Top down preselection of young bulls before entering progeny testing has been proposed as a practicable form of marker-assisted selection (MAS), especially in dairy cattle populations with large male paternal half-sib families. Linkage phase between the superior (Q) and the inferior (q) QTL alleles of heterozygous sires (Qq at the QTL) with informative markers is established within each paternal half-sib family and may be used for selection among grand-progeny. If, additionally to sires, bulldams are also genotyped and data from consecutive generations are used, then a marker-assisted best linear unbiased prediction (MA-BLUP) model can be employed to connect the information of all generations and families of a top down design, and to select across all families. A customized ,augmented' sire model (with sires and dams of sires as random effects) is introduced for this purpose. Adapted formulae for the mixed model equations are given and their equivalence to a corresponding animal model and to a certain variant of previously proposed reduced animal models is shown. The application of the augmented sire model in MA-BLUP estimation from daughter-yield deviations and effective daughter contributions is presented. Zusammenfassung Die Top down Vorselektion von jungen Bullen vor der Nachkommenschaftsprüfung ist bekannt als eine praktikable Form der markergestützten Selektion in Milchrinderpopulationen. Die Kopplungsphasen zwischen dem günstigen (Q) und dem ungünstigen (q) Allel eines QTL heterozygoten Vaters (Qq am QTL) mit den Allelen gekoppelter genetischer Marker werden innerhalb Familien festgestellt und können zur Vorselektion von Enkeln genutzt werden. Wenn zusätzlich zu den Vätern die Mütter genotypisiert sind und Daten von mehreren Generationen vorliegen, können MA-BLUP Modelle genutzt werden, um Informationen von mehreren Familien und mehreren Generationen eines Top down Designs zusammenzuführen und um eine Vorselektion über Familien hinweg vorzunehmen. Hierfür wird ein geeignetes ,erweitertes' Vatermodell eingeführt, welches die Väter und zusätzlich die Mütter der Väter als zufällige Effekte enthält. Angepasste Formeln für die gemischten Modell Gleichungen werden beschrieben. Die Gleichheit dieses erweiterten Vatermodells mit einem entsprechenden Tiermodell und mit einer Variante des reduzierten Tiermodells wird gezeigt. Die Anwendung des erweiterten Vatermodells zur MA-BLUP Schätzung mit daughter yield deviations und effective daughter contributions ist beschrieben. [source] Linkage and QTL mapping for Sus scrofa chromosome 1JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2003P. Beeckmann Summary Linkage maps of Sus scrofa chromosome 1 (SSC1) have been produced using 10 markers in three different F2 families based on crosses of Meishan (M), Pietrain (P) and Wild Boar (W). The maps were similar for the different families and show higher paternal recombination, especially in the interval SW2130,SW803. Quantitative trait loci (QTLs) affecting body conformation, carcass composition, fat deposition and numbers of teats were identified in all three families. Major QTLs were mapped in chromosomal intervals centred at approximately 60, 120 and 170 cM. The QTLs explain up to 8.4% of phenotypic variance in the F2 generation. Pietrain QTL alleles were superior in comparison with Wild Boar and Meishan alleles for most of the trait values. Meishan alleles were associated with highest fat deposition. Additive gene effects were generally larger than dominance effects. QTL profiles on SSC1 differed between families, with the W × P family being most distinct. Zusammenfassung Kopplungskarten für Chromosom 1 (SSC1), die durch die Analyse von 10 Markern erstellt wurden, stimmten in drei untersuchten F2 -Familien (basierend auf Kreuzungen mit Meishan (M), Pietrain (P) und Wildschwein (W)) wie auch mit den bisher publizierten Karten überein. Die geschlechtsspezifischen Karten zeigten eine höhere Frequenz der Rekombinationen in der paternalen Meiose als in der maternalen, besonders im Intervall SW2130 bis SW803. Auf SSC1 konnten bedeutsame QTL-Effekte mit Wirkung auf Wachstum, Schlachtkörperzusammensetzung und Fettansatz sowie die Zitzenzahl in allen drei Familien kartiert werden, insbesondere in den Regionen um 60, 120 und 170 cM. Sie erklärten bis zu 8,4% der phänotypischen Varianz in der F2 -Generation. Pietrain-Allele zeigten positive Auswirkungen auf die meisten Fleischleistungsmerkmale. Meishan-Allele waren mit einer stärkeren Verfettung assoziiert. Es wurden Unterschiede zwischen den QTL-Profilen in den Familien beobachtet, wobei die Familie W × P besonders stark von den QTL-Profilen in den beiden anderen Familien abwich. [source] Linkage and QTL mapping for Sus scrofa chromosome 2JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2003S. S. Lee Summary Sus scrofa chromosome 2 (SSC2) linkage maps were generated from three F2 families involving Meishan (M), Pietrain (P) and Wild Boar (W) crosses and the same 10 marker loci. SSC2 linkage maps were similar between families and correspond to published maps. Quantitative trait loci (QTLs) for carcass traits, daily gain and heart weight were identified on SSC2, especially in the intervals 0,20 and 80,90 cM in the M × P family, the intervals 20,50 and 125,140 cM in the W × P family, and the interval 15,70 cM in the W × M family. QTL presence and position varied among families. QTL effects explained up to 10% of F2 phenotypic variance. Pietrain QTL alleles were associated with high muscle and heart mass, high daily gain and low fat deposition and Meishan alleles with high carcass fat content. Wild Boar alleles were associated with leaner carcass but lower daily gain than Meishan alleles. Zusammenfassung Kopplungskarten für Chromosom 2 (SSC2) wurden auf der Basis von drei F2 -Familien aus Kreuzungen von Wildschwein (W), Meishan (M) und Pietrain (P) mit Hilfe von zehn Markern erstellt. Sie zeigten eine gute Übereinstimmung zwischen den Familien und mit den bisher publizierten Karten. Auf SSC2 ließen sich QTLs mit Wirkung auf Schlachtkörperzusammensetzung, tägliche Zunahmen und Herzgewicht nachweisen. Die QTL-Positionen lagen in der Familie M × P in den Regionen 0 bis 20 cM und 80 bis 90 cM, in der Familie W × P in den Regionen 20 bis 50 cM und 125 bis 140 cM und in der Familie W × M in der Region 15 bis 70 cM. Die QTL-Effekte auf SSC2 erklärten bis zu 10% der phänotypischen Varianz in der F2 -Generation. An den QTL-Positionen zeigten Pietrain-Allele positive Auswirkungen auf die Fleischfülle, das Herzgewicht und die täglichen Zunahmen. Meishan-Allele waren mit einer stärkeren Verfettung assoziiert. Im Vergleich zu Meishan-Allelen traten die Wildschwein-Allele mit höherem Fleischanteil und geringeren täglichen Zunahmen auf. [source] Segregation of infectious pancreatic necrosis resistance QTL in the early life cycle of Atlantic Salmon (Salmo salar)ANIMAL GENETICS, Issue 5 2010A. A. Gheyas Summary In a previous study, three significant quantitative trait loci (QTL) associated with resistance to Infectious Pancreatic Necrosis (IPN) disease were identified by analysing challenge data from one sub-population of Landcatch Atlantic salmon (Salmo salar) smolt. While these QTL were shown to affect the resistance in seawater, their effect in freshwater was unknown. This study investigates the effect of these QTL on IPN resistance in salmon fry in freshwater. Twenty families with intermediate levels of IPN mortality were analysed from a freshwater challenge trial undertaken on a different sup-population of LNS salmon to that studied previously. Only the QTL from linkage group 21 (LG21) appeared to have a significant and large effect on resistance in freshwater; the same QTL was found to have the largest effect in seawater in the previous study. Variance component analysis showed a high heritability for the QTL: 0.45 ± 0.07 on the liability scale and 0.25 ± 0.05 on the observed scale. In a family where both parents were segregating for the QTL, there was a 0% vs. 100% mortality in homozygous offspring for resistant and susceptible QTL alleles. The finding that the same QTL has major effect in both freshwater and seawater has important practical implications, as this will allow the improvement of resistance in both phases through marker assisted selection by targeting this QTL. Moreover, the segregation of the LG21 QTL in a different sub-population gives further evidence of its association with IPN-resistance. [source] Genome-wide identification of quantitative trait loci for carcass composition and meat quality in a large-scale White Duroc × Chinese Erhualian resource populationANIMAL GENETICS, Issue 5 2009J. Ma Summary Carcass and meat quality traits are economically important in pigs. In this study, 17 carcass composition traits and 23 meat quality traits were recorded in 1028 F2 animals from a White Duroc × Erhualian resource population. All pigs in this experimental population were genotyped for 194 informative markers covering the entire porcine genome. Seventy-seven genome-wide significant quantitative trait loci (QTL) for carcass traits and 68 for meat quality were mapped to 34 genomic regions. These results not only confirmed many previously reported QTL but also revealed novel regions associated with the measured traits. For carcass traits, the most prominent QTL was identified for carcass length and head weight at 57 cM on SSC7, which explained up to 50% of the phenotypic variance and had a 95% confidence interval of only 3 cM. Moreover, QTL for kidney and spleen weight and lengths of cervical vertebrae were reported for the first time in pigs. For meat quality traits, two significant QTL on SSC5 and X were identified for both intramuscular fat content and marbling score in the longissimus muscle, while three significant QTL on SSC1 and SSC9 were found exclusively for IMF. Both LM and the semimembranous muscle showed common QTL for colour score on SSC4, 5, 7, 8, 13 and X and discordant QTL on other chromosomes. White Duroc alleles at a majority of QTL detected were favourable for carcass composition, while favourable QTL alleles for meat quality originated from both White Duroc and Erhualian. [source] Power of Linkage Disequilibrium Mapping to Detect a Quantitative Trait Locus (QTL) in Selected Samples of Unrelated IndividualsANNALS OF HUMAN GENETICS, Issue 6 2003A. Tenesa Summary We considered a strategy to map quantitative trait loci (QTLs) using linkage disequilibrium (LD) when the QTL and marker locus were multiallelic. The strategy involved phenotyping a large number of unrelated individuals and genotyping only selected individuals from the two tails of the trait distribution. Power to detect trait-marker association was assessed as a function of the number of QTL and marker alleles. Two patterns of LD were used to study their influence on power. When the frequency of the QTL allele with the largest effect and that of the marker allele linked in coupling were equal, power was maximum. In this case, increasing the number of QTL alleles reduced the power. The maximum difference in power between the two LD patterns studied was ,30%. For low QTL heritabilities (h2QTL < 0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution. [source] |