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QTL Location (qtl + location)

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Life Sciences	50%

Selected Abstracts

MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component

GENETIC EPIDEMIOLOGY, Issue 2 2007
Yun Ju Sung
Abstract We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source]

Accounting for uncertainty in QTL location in marker-assisted pre-selection of young bulls prior to progeny test

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2002
A. STELLA
The objective of this study was to evaluate whether the efficacy of marker assisted selection (MAS) could be improved by considering a confidence interval (CI) of QTL position. Specifically, MAS was applied for within-family selection in a stochastic simulation of a closed nucleus herd. The location and effect of the QTL were estimated by least squares interval mapping with a granddaughter design and marker information was then used in a top down scheme. Three approaches were used to select the best bull within full sibships of 3 or 40 bulls. All three were based on the probability of inheriting the favorable allele from the grandsire (PROB). The first method selected the sib with the highest PROB at the location with the highest F-ratio (MAX). The other two approaches were based on sums of estimated regression coefficients weighted by PROB at each cM within a 95% CI based on either bootstrapping (BOOT) or approximate LOD scores (LOD). Accounting for CI increased the relative genetic gain in all scenarios. The average breeding value (BV) of the selected bulls was increased by 2.00, 2.60 and 2.59% when MAS was applied using MAX, BOOT and LOD, respectively, compared to random selection (h2=0.30). Selected bulls carried the correct allele in 63.0, 68.5, 67.6 and 50.1% of the cases for MAX, BOOT, LOD and random selection, respectively. Berü;cksichtigung der Unsicherheit von QTL Positionen bei Marker-gestü;tzter Vorselektion von jungen Bullen vor der Nachkommenprü;fung Das Ziel dieser Studie war es zu prüfen, ob die Effizienz von MAS (Marker gestützte Selektion) durch Berücksichtigung des Konfidenzintervalls (CI) einer QTL Position verbessert werden kann. Es wurde MAS bei der Selektion innerhalb Familien in einer geschlossenen Nukleus Herde in einer stochastischen Simulation angewandt. Die Postition und der Effekt des QTL wurden in einem Granddaughter Design mit einer Least=Square Intervall Kartierung geschätzt. Die Marker Informationen wurden dann in einem top-down-Schema verwendet. Drei Ansätze fanden Verwendung, um den besten Bullen innerhalb von Vollgeschwistern von 3 oder 40 Bullen zu selektieren. Alle drei Ansätze basieren auf der Wahrscheinlichkeit, ein zu bevorzugendes Allel vom Grossvater zu erben (PROB). Bei der ersten Methode wurden die Geschwister mit der höchsten PROB an der Position mit dem höchsten F-Wert selektiert (MAX). Die beiden anderen Ansätze basierten auf den Summen der geschätzten Regressionskoeffizienten, gewichtet nach PROB an jedem cM innerhalb eines 95%igen CI, das entweder auf Bootstrapping (BOOT) oder approximativen LOD Scores (LOD) basiert. Die Berücksichtigung des CI vergrösserte den relativen genetischen Fortschritt in allen Szenarien. Bei Anwendung von MAS waren die durchschnittlichen Zuchtwerte der selektierten Bullen bei Verwendung von MAX, BOOT und LOD verglichen mit zufälliger Selektion (h2=0,30) um 2,00, 2,60 und 2,59% gestiegen. Die selektierten Bullen trugen das richtige Allel bei den entsprechenden Berechnungen MAX, BOOT, LOD und zufälliger Selektion in 63,0, 68,5, 67,6 und 50,1% der Fälle. [source]

Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designs

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2001
H. N. Kadarmideen
A Generalized Marker Regression Mapping (GMR) approach was developed for mapping Quantitative Trait Loci (QTL) affecting binary polygenic traits in a single-family half-sib design. The GMR is based on threshold-liability model theory and regression of offspring phenotype on expected marker genotypes at flanking marker loci. Using simulation, statistical power and bias of QTL mapping for binary traits by GMR was compared with full QTL interval mapping based on a threshold model (GIM) and with a linear marker regression mapping method (LMR). Empirical significance threshold values, power and estimates of QTL location and effect were identical for GIM and GMR when QTL mapping was restricted to within the marker interval. These results show that the theory of the marker regression method for QTL mapping is also applicable to binary traits and possibly for traits with other non-normal distributions. The linear and threshold models based on marker regression (LMR and GMR) also resulted in similar estimates and power for large progeny group sizes, indicating that LMR can be used for binary data for balanced designs with large families, as this method is computationally simpler than GMR. GMR may have a greater potential than LMR for QTL mapping for binary traits in complex situations such as QTL mapping with complex pedigrees, random models and models with interactions. Generalisierte Marker Regression und Intervall QTL Kartierungsmethoden für binäre Merkmale in einem Halbgeschwisterdesign Es wurde ein Ansatz zur generalisierten Marker Regressions Kartierung (GMR) entwickelt, um quantitative Merkmalsloci (QTL) zu kartieren, die binäre polygenetische Merkmale in einem Einfamilien-Halbgeschwisterdesign beeinflussen. Das GMR basiert auf der Theorie eines Schwellenwertmodells und auf der Regression des Nachkommenphänotyps auf den erwarteten Markergenotyp der flankierenden Markerloci. Mittels Simulation wurde die statistische Power und Schiefe der QTL Kartierung für binäre Merkmale nach GMR verglichen mit vollständiger QTL Intervallkartierung, die auf einem Schwellenmodell (GIM) basiert, und mit einer Methode zur linearen Marker Regressions Kartierung (LMR). Empirische Signifikanzschwellenwerte, Power und Schätzer für die QTL Lokation und der Effekt waren für GIM und GMR identisch, so lange die QTL Kartierung innerhalb des Markerintervalls definiert war. Diese Ergebnisse zeigen, dass die Theorie der Marker Regressions-Methode zur QTL Kartierung auch für binäre Merkmale und möglicherweise auch für Merkmale, die keiner Normalverteilung folgen, geeignet ist. Die linearen und Schwellenmodelle, die auf Marker Regression (LMR und GMR) basieren, ergaben ebenfalls ähnliche Schätzer und Power bei großen Nachkommengruppen, was schlussfolgern lässt, dass LMR für binäre Daten in einem balancierten Design mit großen Familien genutzt werden kann. Schließlich ist diese Methode computertechnisch einfacher als GMR. GMR mag für die QTL Kartierung bei binären Merkmalen in komplexen Situationen ein größeres Potential haben als LMR. Ein Beispiel dafür ist die QTL Kartierung mit komplexen Pedigrees, zufälligen Modellen und Interaktionsmodellen. [source]

Genome-wide scan for bovine twinning rate QTL using linkage disequilibrium

ANIMAL GENETICS, Issue 3 2009
E.-S. Kim
Summary Twinning is a complex trait with negative impacts on health and reproduction, which cause economic loss in dairy production. Several twinning rate quantitative trait loci (QTL) have been detected in previous studies, but confidence intervals for QTL location are broad and many QTL are unreplicated. To identify genomic regions or genes associated with twinning rate, QTL analysis based on linkage combined with linkage disequilibrium (LLD) and individual marker associations was conducted across the genome using high-throughput single nucleotide polymorphism (SNP) genotypes. A total of 9919 SNP markers were genotyped with 200 sires and sons in 19 half-sib North American Holstein dairy cattle families. After SNPs were genotyped, informative markers were selected for genome-wide association tests and QTL searches. Evidence for twinning rate QTL was found throughout the genome. Thirteen markers significantly associated with twinning rate were detected on chromosomes 2, 5 and 14 (P < 2.3 × 10,5). Twenty-six regions on fourteen chromosomes were identified by LLD analysis at P < 0.0007. Seven previously reported ovulation or twinning rate QTL were supported by results of single marker association or LLD analyses. Single marker association analysis and LLD mapping were complementary tools for the identification of putative QTL in this genome scan. [source]