Simulation Comparisons of Effectiveness among QTL Mapping Procedures of Different Statistical Genetic Models

Su, Chengfu; Zhao, Tuanjie; Gai, Junyi

doi:10.3724/sp.j.1006.2010.01100

Cited by 19 publications

(18 citation statements)

References 7 publications

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“…Among these, MQM and CIM could only detect the additive and dominant effects, while ICIM and MIMR could be applied to estimate the additive, dominant, and epistatic effects. The obtained results, which shared some similar aspects with the results of a previous study (Su et al, 2010b and2013), revealed the importance of selecting the appropriate QTL mapping procedure for different populations, using different genetic models. Many research groups have previously analyzed the QTL mapping accuracy.…”

Section: Discussionsupporting

confidence: 84%

Influence of various quantitative trait loci (QTL) mapping methods on the mapping accuracy under varying heritability levels

Cf¹,

Zb²,

Li³

2015

Genet. Mol. Res.

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ABSTRACT. The study of quantitative trait effects is of great significance for molecular marker-assisted breeding. The accuracy of quantitative trait loci (QTL) mapping is the key factor affecting marker-assisted breeding, and is extremely significant. The effect of different heritability rates (10, 30, 50, 70, and 90%) on the accuracy of QTL mapping of five recombinant inbred lines (RILs) were analyzed via computer simulation. RILs display additive and epistatic genetic effects. The QTLs were analyzed using four different mapping procedures: multiple QTL model (MQM), composite interval mapping (CIM), multiple interval mapping (MIMR), and inclusive composite interval mapping (ICIM). The results revealed an increase in the QTL mapping accuracy and QTL detection power, and a decrease in the QTL interval range with the increase in heritability; conversely, an irregular number of false positive QTLs were generated. CIM and MQM only screen the additive and dominant effects; MIMR and ICIM screen the additive, dominant, and epistatic effects. The highest QTL detection power obtained using MQM and CIM was only 75%, while MIMR and ICIM showed a detection power of 100%. At heritability rates of more than 50 and less than 10%, the detection powers of the MIMR and ICIM procedures were >95 13004 C.F. Su et al.©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (4): 13003-13012 (2015) and <35%, respectively. QTL mapping has no significance at heritability rates <10%. The results of this study suggest that QTL mapping has significance at a heritability rate >30% (at least >10%) for practical marker-assisted breeding.

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Section: Discussionsupporting

confidence: 84%

Influence of various quantitative trait loci (QTL) mapping methods on the mapping accuracy under varying heritability levels

Cf¹,

Zb²,

Li³

2015

Genet. Mol. Res.

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“…Strategy of QTL mapping with multiple models proposed by Su et al (2010b) was adopted. Three QTL mapping methods, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Besides, the mapping method also intimately affects the mapping results, and an inappropriate mapping method may result in erroneous judgment or false-positive (Su et al, 2010a). Composite interval mapping (CIM) and multiple QTL mapping (MQM) are suitable to detect QTLs when the data fit the genetic model of y = μ + a 1 + e, and multiple interval mapping with regression forward selection (MIMR), multiple interval mapping with forward search (MIMF) and inclusive composite interval mapping (ICIM) are suitable to detect QTLs when the data fit the genetic models of y = μ + a 1 + e and y = μ + a 1 + a 1 + a 1 a 2 + e, while composite interval mapping based on mixed linear model (MCIM) fits all models (Su et al, 2010b). Seven quality traits (GL, LWR, CGR, CD, GT, AC and GC) were analyzed using mixed major gene and polygene inheritance model, and results showed that epistatic effects were found in the genetic variation of those traits (Jiang et al, 2007).…”

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confidence: 99%

QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

et al. 2013

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QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines (RIL) derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length (GL), grain length to width ratio (LWR), chalk grain rate (CGR), chalkiness degree (CD), gelatinization temperature (GT), amylose content (AC) and gel consistency (GC) of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model (MCIM), inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model (ICIM) and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis (MIMR). Results showed that five QTLs with additive effect (A-QTLs) were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect (E-QTLs) detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.

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“…In a recent study by Su et al (2010), the recombinant inbred line (RIL) populations were simulated based on four kinds of genetic models, including model I, additive QTL; model II, additive and epistatic QTL; model III, additive QTL and QTL 9 environment interaction, and model IV, additive QTL, epistatic QTL and QTL 9 environment interaction. Two sets of RIL data for each of the four models, in a total of eight sets of RIL data, were simulated and analyzed with the six popularly-used QTL mapping procedures, i.e.…”

Section: Introductionmentioning

confidence: 99%

Genetic structure composed of additive QTL, epistatic QTL pairs and collective unmapped minor QTL conferring oil content and fatty acid components of soybeans

Zhao

Wang

et al. 2011

Euphytica

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The relative importance of various types of quantitative trait locus (QTL) conferring oil content and its fatty acid components in soybean seeds was assessed through testing a recombinant inbred line (RIL) population (derived from KF1 9 NN1138-2) in randomized blocks experiments in [2004][2005][2006]. The contents of oil and oleic, linoleic, linolenic, palmitic and stearic acids were determined with automatic Soxhlet extraction system and gas chromatography, respectively. Based on the established genetic linkage map with 834 markers, QTLNetwork2.0 was used to detect QTL under the genetic model composed of additive, additive 9 additive (epistasis), additive 9 year and epistasis 9 year effects. The contributions to the phenotypic variances of additive QTL and epistatic QTL pairs were 15.7% (3 QTL) and 10.8% (2 pairs) for oil content, 10.4% (3 QTL) and 10.3% (3 pairs) for oleic acid, 11.6% (3 QTL) and 8.5% (2 pairs) for linoleic acid, 28.5% (7 QTL) and 7.6% (3 pairs) for linolenic acid, 27.0% (6 QTL) and 16.6% (7 pairs) for palmitic acid and 29.7% (5 QTL) and 4.3% (1 pair) for stearic acid, respectively. Those of additive QTL by year interaction were small and no epistatic QTL pair by year interaction was found. Among the 27 additive QTL and 36 epistatic QTL (18 pairs), three are duplicated between the two QTL types. A large difference was found between the genotypic variance among RILs and the total variance of mapped QTL, which accounted for 52.9-74.8% of the genotypic variation, much larger than those of additive QTL and epistatic QTL pairs. This part of variance was recognized as that due to a collection of unmapped minor QTL, like polygenes in biometrical genetics, and was designated as collective unmapped minor QTL. The results challenge the breeders for how to pyramid different types of QTL. In addition, the present study supports the mapping strategy of a full model scanning followed by verification with other procedures corresponding to the first results.

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Simulation Comparisons of Effectiveness among QTL Mapping Procedures of Different Statistical Genetic Models

Cited by 19 publications

References 7 publications

Influence of various quantitative trait loci (QTL) mapping methods on the mapping accuracy under varying heritability levels

Influence of various quantitative trait loci (QTL) mapping methods on the mapping accuracy under varying heritability levels

QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

Genetic structure composed of additive QTL, epistatic QTL pairs and collective unmapped minor QTL conferring oil content and fatty acid components of soybeans

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