QTL detection in maize testcross progenies as affected by related and unrelated testers

The evaluation of recombinant inbred lines (RILs) per se can be biased by inbreeding depression in case of allogamous species. To overcome this drawback, RILs can be evaluated in combination with testers; however, testers can carry dominant alleles at the quantitative trait loci (QTL), thus hampering their detection. This study was conducted on the maize (Zea mays L.) population of 142 RILs derived from the single cross B73 × H99 to evaluate the role of different testers in affecting: (1) QTL detection, (2) the estimates of their effects, and (3) the consistency of such estimates across testers. Testcrosses (TCs) were produced by crossing RILs with inbred testers B73 [TC(B)], H99 [TC(H)], and Mo17 [TC(M)]. TCs were field tested in three environments. TC(B) mean was higher than TC(H) mean for all traits, while TC(M) mean was the highest for plant vigor traits and grain yield. As to the number of detected QTL, tester Mo17 was superior to H99 and B73 for traits with prevailing additive effects. Several overlaps among the QTL were detected in two or all the three TC populations with QTL effects being almost always consistent (same sign). For traits with prevailing dominance–overdominance effects, as grain yield, the poor performing tester H99 was clearly the most effective; fewer overlaps were found and some of them were inconsistent (different sign). Epistatic interactions were of minor importance. In conclusion, the three testers proved to affect QTL detection and estimation of their effects, especially for traits showing high dominance levels. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Bias and Sampling Error of the Estimated Proportion of Genotypic Variance Explained by Quantitative Trait Loci Determined From Experimental Data in Maize Using Cross Validation and Validation With Independent Samples

Cross validation (CV) was used to analyze the effects of different environments and different genotypic samples on estimates of the proportion of genotypic variance explained by QTL (p). Testcrosses of 344 F(3) maize lines grown in four environments were evaluated for a number of agronomic traits. In each of 200 replicated CV runs, this data set was subdivided into an estimation set (ES) and various test sets (TS). ES were used to map QTL and estimate p for each run (p(ES)) and its median (p(ES)) across all runs. The bias of these estimates was assessed by comparison with the median (p(TS.ES)) obtained from TS. We also used two independent validation samples derived from the same cross for further comparison. The median p(ES) showed a large upward bias compared to p(TS.ES). Environmental sampling generally had a smaller effect on the bias of p(ES) than genotypic sampling or both factors simultaneously. In independent validation, p(TS.ES) was on average only 50% of p(ES). A wide range among p(ES) reflected a large sampling error of these estimates. QTL frequency distributions and comparison of estimated QTL effects indicated a low precision of QTL localization and an upward bias in the absolute values of estimated QTL effects from ES. CV with data from three QTL studies reported in the literature yielded similar results as those obtained with maize testcrosses. We therefore recommend CV for obtaining asymptotically unbiased estimates of p and consequently a realistic assessment of the prospects of MAS.     

Bias and Sampling Error of the Estimated Proportion of Genotypic Variance Explained by Quantitative Trait Loci Determined From Experimental Data in Maize Using Cross Validation and Validation With Independent Samples.

Cross validation (CV) was used to analyze the effects of different environments and different genotypic samples on estimates of the proportion of genotypic variance explained by QTL (p). Testcrosses of 344 F(3) maize lines grown in four environments were evaluated for a number of agronomic traits. In each of 200 replicated CV runs, this data set was subdivided into an estimation set (ES) and various test sets (TS). ES were used to map QTL and estimate p for each run (p(ES)) and its median (p(ES)) across all runs. The bias of these estimates was assessed by comparison with the median (p(TS.ES)) obtained from TS. We also used two independent validation samples derived from the same cross for further comparison. The median p(ES) showed a large upward bias compared to p(TS.ES). Environmental sampling generally had a smaller effect on the bias of p(ES) than genotypic sampling or both factors simultaneously. In independent validation, p(TS.ES) was on average only 50% of p(ES). A wide range among p(ES) reflected a large sampling error of these estimates. QTL frequency distributions and comparison of estimated QTL effects indicated a low precision of QTL localization and an upward bias in the absolute values of estimated QTL effects from ES. CV with data from three QTL studies reported in the literature yielded similar results as those obtained with maize testcrosses. We therefore recommend CV for obtaining asymptotically unbiased estimates of p and consequently a realistic assessment of the prospects of MAS.     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

不同QTL增效基因初始频率下标记辅助选择的效果

采用随机模拟方法模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形。在假定选择性状受一个位于常染色体上的QTL和多基因共同控制的情况下,采用动物模型标记辅助最佳线性无偏预测方法估计个体育种值并据此进行种畜的选留,并在此基础上系统地比较了QTL增效基因初始频率对标记辅助选择效果的影响。结果表明：当群体中QTL增效基因的初始频率较低时,选择所获得的QTL基因型值的进展会更大,标记辅助选择在单位时间内可获得较大的遗传进展;此时,尽管QTL增效基因在群体中固定所需的世代数会更长一些,但其频率上升的速度却更快。而QTL增效基因初始频率的高低对群体近交增量的影响不是很大。     

Identification of quantitative trait loci across recombinant inbred lines and testcross populations for traits of agronomic importance in rice

This study was conducted to determine whether quantitative trait loci (QTL) controlling traits of agronomic importance detected in recombinant inbred lines (RILs) are also expressed in testcross (TC) hybrids of rice. A genetic map was constructed using an RIL population derived from a cross between B5 and Minghui 63, a parent of the most widely grown hybrid rice cultivar in China. Four TC hybrid populations were produced by crossing the RILs with three maintaining lines for the widely used cytoplasmic male-sterile (CMS) lines and the genic male-sterile line Peiai64s. The mean values of the RILs for the seven traits investigated were significantly correlated to those of the F1 hybrids in the four TC populations. Twenty-seven main-effect QTL were identified in the RILs. Of these, the QTL that had the strongest effect on each of the seven traits in the RILs was detected in two or more of the TC populations, and six other QTL were detected in one TC population. Epistatic analysis revealed that the effect of epistatic QTL was relatively weak and cross combination specific. Searching publicly available QTL data in rice revealed the positional convergence of the QTL with the strongest effect in a wide range of populations and under different environments. Since the main-effect QTL is expressed across different testers, and in different genetic backgrounds and environments, it is a valuable target for gene manipulation and for further application in rice breeding. When a restorer line that expresses main-effect QTL is bred, it could be used in a number of cross combinations.     

QTL detection and allelic effects for growth and fat traits in outbred pig populations

Quantitative trait loci (QTL) for growth and fatness traits have previously been identified on chromosomes 4 and 7 in several experimental pig populations. The segregation of these QTL in commercial pigs was studied in a sample of 2713 animals from five different populations. Variance component analysis (VCA) using a marker-based identity by descent (IBD) matrix was applied. The IBD coefficient was estimated with simple deterministic (SMD) and Markov chain Monte Carlo (MCMC) methods. Data for two growth traits, average daily gain on test and whole life daily gain, and back fat thickness were analysed. With both methods, seven out of 26 combinations of population, chromosome and trait, were significant. Additionally, QTL genotypic and allelic effects were estimated when the QTL effect was significant. The range of QTL genotypic effects in a population varied from 4.8% to 10.9% of the phenotypic mean for growth traits and 7.9% to 19.5% for back fat trait. Heritabilities of the QTL genotypic values ranged from 8.6% to 18.2% for growth traits, and 14.5% to 19.2% for back fat. Very similar results were obtained with both SMD and MCMC. However, the MCMC method required a large number of iterations, and hence computation time, especially when the QTL test position was close to the marker.     

QTL mapping for European corn borer resistance (<Emphasis Type="Italic">Ostrinia nubilalis</Emphasis> Hb.), agronomic and forage quality traits of testcross progenies in early-maturing European maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.) germplasm

In hybrid breeding the performance of lines in hybrid combinations is more important than their performance per se. Little information is available on the correlation between individual line and testcross (TC) performances for the resistance to European corn borer (ECB, Ostrinia nubilalis Hb.) in maize (Zea mays L.). Marker assisted selection (MAS) will be successful only if quantitative trait loci (QTL) found in F₂ derived lines for ECB resistance are still expressed in hybrid combinations. The objectives of our study were: (1) to identify and characterize QTL for ECB resistance as well as agronomic and forage quality traits in a population of testcrossed F_2:3 families; (2) to evaluate the consistency of QTL for per se and TC performances; and (3) to determine the association between per se and TC performances of F_2:3 lines for these traits. Two hundred and four F_2:3 lines were derived from the cross between maize lines D06 (resistant) and D408 (susceptible). These lines were crossed to D171 and the TC progenies were evaluated for ECB resistance and agronomic performance in two locations in 2000 and 2001. Using these TC progenies, six QTL for stalk damage rating (SDR) were found. These QTL explained 27.4% of the genotypic variance in a simultaneous fit. Three QTL for SDR were detected consistently for per se and TC performance. Phenotypic and genotypic correlations were low for per se and TC performance for SDR. Correlations between SDR and quality traits were not significant. Based on these results, we conclude that MAS will not be an efficient method for improving SDR. However, new molecular tools might provide the opportunity to use QTL data as a first step to identify genes involved in ECB resistance. Efficient MAS procedures might then be based on markers designed to trace and to combine specific genes and their alleles in elite maize breeding germplasm.Communicated by G. Wenzel     

孙女设计中标记密度对QTL定位精确性的影响

采用蒙特卡罗方法分析了在孙女设计中不同的嫩体结构、性状遗传力、ＱＴＬ效应大小和ＱＴＬ在染色体上的位置中个因素不同水平组合下４种标记密度（标记间隔５ｃＭ,１０ｃＭ,２０ｃＭ、５０ｃＭ对ＱＴＬ定位精确性（以均方误ＭＳＥ为衡量指标）的影响,并从经济学角度探讨了应用于标记辅助选（ＭＡＳ）的ＱＴＬ定位的最佳标记密度。结果表明,一般说来,在各因素水平都较低时,ＭＳＥ随标记密度加大而下降的相对幅度也较 小,反之     

Analysis of factors influencing milling yield and their association to other traits by QTL analysis in two hexaploid oat populations

Milling yield, or the grain weight from which 100 kg of rolled groats is obtained upon milling, is an important quality characteristic of cultivated oat (Avena sativa L.). Kernel morphology and the groat (caryopsis) percentage of the whole kernel including hull are factors that influence milling yield. We mapped QTLs for kernel area, kernel length, kernel width, and groat percentage in two populations of 137 recombinant inbred lines by RFLP and AFLP analysis to evaluate the prospects of marker-assisted selection (MAS). Phenotypic correlations between kernel morphology traits and groat percentage were not significant. For kernel morphology traits and groat percentage, one to five QTLs were detected, explaining 7.0–60.7% of the total phenotypic variance depending on the trait. One QTL for kernel length in each population and one QTL for kernel width in one population were found at the same location as a QTL for groat percentage, indicating that a change in kernel size or shape could have an influence on groat percentage. The positions and effects of QTLs for kernel morphology and groat percentage were compared to QTLs detected previously for chemical grain composition (oil andβ-glucanconcentration) and agronomic traits to evaluate the selection response on these traits through MAS. Several regions of the oat genome were identified that contained clusters of QTLs influencing two or more traits. While the allele from one parent at a QTL could simultaneously improve two or more traits in one population, it could have opposite effects on the same traits at another QTL or in the other population. Associations among traits were complex and will require careful consideration when employing QTL-marker associations in MAS to avoid negative selection response. Future research to discover candidate genes for those QTL clusters could provide information about trait associations and help in designing selection programs. Received: 17 February 2000 / Accepted: 27 October 2000     

番茄可溶性固形物的动态QTL及相关性状

利用TD22×HT-1-1-1-1组合产生的206个F2:3家系进行番茄可溶性固形物及相关性状的动态QTL定位,并对3个时期的可溶性固形物与果重、果型指数、可溶性糖、VC及有机酸进行相关性分析.结果表明在番茄果实发育的3个时期可溶性固形物的QTL位点存在差异,绿熟期和红熟期分别检测到4个、8个QTL位点,呈现动态变化,两个时期同时检测到LEαat006和Tomato|TC162363两个标记,对辅助选择育种有重要意义.研究发现3个时期可溶性固形物存在极显著差异,不同时期可溶性固形物的主要相关性状不同.绿熟期与可洛性糖呈显著正相关,与果重呈显著负相关;黄熟期与可溶性糖和有机酸呈显著正相关;红熟期与可溶性糖和有机酸呈显著正相关,与果重呈显著负相关.在各性状相关性分析的基础上建立线性回归模型,利用非参品种进行检验,拟合度达到95%以上.     

海岛棉产量和早熟性相关性状的QTLs定位

对海岛棉产量和早熟性状进行QTL初步定位,为分子标记辅助育种提供依据。利用5200多对SSR引物筛选海岛棉品种新海3号和Giza82间的多态性引物,获得107对。以多态性引物检测新海3号×Giza82的190个F2:3家系,获得120个多态性位点。利用JoinMap3.0分析软件构建了一个包含22个连锁群,74个标记,标记间平均距离12.06 cM,全长893 cM,覆盖海岛棉基因组20.12%的分子标记遗传连锁图谱。采用复合区间作图法检测到21个与海岛棉产量性状和早熟性状有关的QTL,其中早熟性状检测到12个QTL,分别位于1、3、5、6、11、17、22共7个连锁群上;产量性状检测到9个QTL,分别位于1、4、5、6、7、16、22共7个连锁群上。研究结果为海岛棉产量性状和早熟性状的分子设计育种提供了有用的信息。     

QTL mapping of <Emphasis Type="Italic">Sclerotinia</Emphasis> midstalk-rot resistance in sunflower

In many sunflower-growing regions of the world, Sclerotinia sclerotiorum (Lib.) de Bary is the major disease of sunflower (Helianthus annuus L.). In this study, we mapped and characterized quantitative trait loci (QTL) involved in resistance to S. sclerotiorum midstalk rot and two morphological traits. A total of 351 F₃ families developed from a cross between a resistant inbred line from the germplasm pool NDBLOS and the susceptible line CM625 were assayed for their parental F₂ genotype at 117 codominant simple sequence repeat markers. Disease resistance of the F₃ families was screened under artificial infection in field experiments across two sowing times in 1999. For the three resistance traits (leaf lesion, stem lesion, and speed of fungal growth) and the two morphological traits, genotypic variances were highly significant. Heritabilities were moderate to high (h²=0.55–0.89). Genotypic correlations between resistance traits were highly significant (P<0.01) but moderate. QTL were detected for all three resistance traits, but estimated effects at most QTL were small. Simultaneously, they explained between 24.4% and 33.7% of the genotypic variance for resistance against S. sclerotiorum. Five of the 15 genomic regions carrying a QTL for either of the three resistance traits also carried a QTL for one of the two morphological traits. The prospects of marker-assisted selection (MAS) for resistance to S. sclerotiorum are limited due to the complex genetic architecture of the trait. MAS can be superior to classical phenotypic selection only with low marker costs and fast selection cycles.     

Different functions of QTL for estrogen-dependent tumor growth of the rat pituitary

Estrogen treatment to rats of the Fischer 344 (F344) strain induces growth of pituitary tumors that exhibit accelerated cell proliferation, breakdown of basement membrane, and formation of hemorrhagic lakes. Estrogen-dependent pituitary growth is due to variation in a group of quantitative trait loci (QTL), called Edpm for estrogen-dependent pituitary mass, that we previously identified in an F₂ intercross of F344 and the tumor-resistant Brown Norway strain. We previously identified 5 QTL, and microsatellite markers developed since our earlier work have allowed us to scan new chromosomal regions, resulting in two new QTL for estrogen-dependent pituitary mass: Edpm9-2 and a possible QTL on the X Chromosome (Chr). Here we report evidence that these QTL differ from each other in how they affect growth. To examine the effect of the Edpm QTL on biochemical components of tumor growth, we tested their effects in 138 progeny of a backcross to the F344 strain which were given a 10-week chronic estrogen treatment. Hemoglobin/DNA ratio (a measure of blood volume relative to cell number) and total pituitary DNA (a measure of cell number) correlated only weakly, and very large pituitaries were observed which had a low hemoglobin/DNA ratio resembling a normal gland. Through QTL mapping, we found that Edpm2-1, Edpm3, Edpm5, and Edpm9-2 all had significant effects on pituitary mass, but Edpm2-1 and Edpm9-2 primarily affected DNA content, Edpm5 primarily affected hemoglobin/DNA ratio, and Edpm3 affected all traits equally. Received: 16 March 2000 / Accepted: 17 May 2000     

Mapping of QTL associated with waterlogging tolerance during the seedling stage in maize

BACKGROUND AND AIMS: Soil waterlogging is a major environmental stress that suppresses maize (Zea mays) growth and yield. To identify quantitative trait loci (QTL) associated with waterlogging tolerance at the maize seedling stage, a F2 population consisting of 288 F(2:3) lines was created from a cross between two maize genotypes, 'HZ32' (waterlogging-tolerant) and 'K12' (waterlogging-sensitive). METHODS: The F2 population was genotyped and a base-map of 1710.5 cM length was constructed with an average marker space of 11.5 cM based on 177 SSR (simple sequence repeat) markers. QTL associated with root length, root dry weight, plant height, shoot dry weight, total dry weight and waterlogging tolerance coefficient were identified via composite interval mapping (CIM) under waterlogging and control conditions in 2004 (EXP.1) and 2005 (EXP.2), respectively. KEY RESULTS AND CONCLUSIONS: Twenty-five and thirty-four QTL were detected in EXP.1 and EXP.2, respectively. The effects of each QTL were moderate, ranging from 3.9 to 37.3 %. Several major QTL determining shoot dry weight, root dry weight, total dry weight, plant height and their waterlogging tolerance coefficient each mapped on chromosomes 4 and 9. These QTL were detected consistently in both experiments. Secondary QTL influencing tolerance were also identified and located on chromosomes 1, 2, 3, 6, 7 and 10. These QTL were specific to particular traits or environments. Although the detected regions need to be mapped more precisely, the findings and QTL found in this study may provide useful information for marker-assisted selection (MAS) and further genetic studies on maize waterlogging tolerance.     

Impacts of QTL × Environment Interactions on Genetic Response to Marker-Assisted Selection

Genotype × environment (GE) interaction is a common characteristic for quantitative traits, and has been a subject of great concern for breeding programs. Simulation studies were conducted to investigate the effects of GE interaction on genetic response to marker-assisted selection (MAS). In our study we demonstrated that MAS is generally more efficient than phenotypic selection in the presence of GE interaction, and this trend is more pronounced for developing broadly adaptable varieties. The utilization of different QTL information dramatically influences MAS efficiency. When MAS is based on QTLs evaluated in a single environment, the causal QTL × environment (QE) interactions usually reduce general response across environments, and the reduction in the cumulative general response is a function of the proportion of QE interactions for the trait studied. However, MAS using QTL information evaluated in multiple environments not only yields higher general response, but the general response obtained is also reasonably robust to QE interactions. The total response achieved by MAS in a specific environment depends largely on the total heritability of traits and is slightly subject to relative changes between general heritability and GE interaction heritability. Two breeding strategies, breeding experiments conducted in one environment throughout and in two environments alternately, were also examined for the implementation of marker-based selection. It was thus concluded that plant breeders should be cautious to utilize QTL information from only one environment and execute breeding studies in another.     

Validation of quantitative trait loci for Fusarium head blight and kernel discoloration in barley

Validation of quantitative trait loci (QTLs) is a prerequisite to marker assisted selection (MAS), however, only a fraction of QTLs identified for important plant traits have been independently tested for validation. Resistance to the diseases kernel discoloration (KD) and Fusarium head blight (FHB) in barley is complex and technically difficult to assess, and therefore QTLs for these traits are suitable targets for MAS. We selected two lines, from a QTL mapping population created using the resistant variety Chevron, and crossed them to susceptible parents to generate two validation populations. Genetic maps of both populations were developed for five chromosomes covering 15 selected regions containing QTLs for FHB severity, KD score and concentration of deoxynivalenol (DON), a mycotoxin produced by the FHB pathogen. QTL analyses using these validation populations confirmed that five of the possible 15 QTL regions were associated with at least one of the three traits. While some QTL were detected inconsistently across environments, QTL that could be subjected to validation in both populations were confirmed in both populations in seven out of eight instances. A QTL for KD score on chromosome 6(6H) was confirmed in both validation populations in eight of nine environments and was also associated with FHB in three of six environments. A QTL for FHB on chromosome 2(2H) was confirmed and was also associated with KD and heading date. Marker assisted selection at these two QTLs should enhance disease resistance, however, the QTL on chromosome 2(2H) will also delay heading date.     

QTL analysis of shading sensitive related traits in maize under two shading treatments

During maize development and reproduction, shading stress is an important abiotic factor influencing grain yield. To elucidate the genetic basis of shading stress in maize, an F(2:3) population derived from two inbred lines, Zhong72 and 502, was used to evaluate the performance of six traits under shading treatment and full-light treatment at two locations. The results showed that shading treatment significantly decreased plant height and ear height, reduced stem diameter, delayed day-to-tassel (DTT) and day-to-silk (DTS), and increased anthesis-silking interval (ASI). Forty-three different QTLs were identified for the six measured traits under shading and full light treatment at two locations, including seven QTL for plant height, nine QTL for ear height, six QTL for stem diameter, seven QTL for day-to-tassel, six QTL for day-to-silk, and eight QTL for ASI. Interestingly, three QTLs, qPH4, qEH4a, and qDTT1b were detected under full sunlight and shading treatment at two locations simultaneously, these QTL could be used for selecting elite hybrids with high tolerance to shading and high plant density. And the two QTL, qPH10 and qDTS1a, were only detected under shading treatment at two locations, should be quit for selecting insensitive inbred line in maize breeding procedure by using MAS method.     

Application of non-parametric bootstrap methods to estimate confidence intervals for QTL location in a beef cattle QTL experimental population

Empirical confidence intervals (CIs) for the estimated quantitative trait locus (QTL) location from selective and non-selective non-parametric bootstrap resampling methods were compared for a genome scan involving an Angus x Brahman reciprocal fullsib backcross population. Genetic maps, based on 357 microsatellite markers, were constructed for 29 chromosomes using CRI-MAP V2.4. Twelve growth, carcass composition and beef quality traits (n = 527-602) were analysed to detect QTLs utilizing (composite) interval mapping approaches. CIs were investigated for 28 likelihood ratio test statistic (LRT) profiles for the one QTL per chromosome model. The CIs from the non-selective bootstrap method were largest (87 7 cM average or 79-2% coverage of test chromosomes). The Selective II procedure produced the smallest CI size (42.3 cM average). However, CI sizes from the Selective II procedure were more variable than those produced by the two LOD drop method. CI ranges from the Selective II procedure were also asymmetrical (relative to the most likely QTL position) due to the bias caused by the tendency for the estimated QTL position to be at a marker position in the bootstrap samples and due to monotonicity and asymmetry of the LRT curve in the original sample.     

QTL mapping for maize resistance and yield under infestation with Sesamia nonagrioides

The Mediterranean corn borer (MCB) is the most important maize insect pest in the Mediterranean region. The main objective was to map quantitative trait loci (QTL) for yield performance under infestation with MCB, resistance and agronomic traits in a maize RIL population derived from an inbred cross European flint × Reid. Six QTL for resistance traits were located: one QTL for tunnel length (bin 9.03; p = 19.8 %), one QTL for stalk lodging (bin 3.07, p = 11.5 %), and four QTL for ear resistance (bins 1.07, 5.03/5.05, and 8.04; p = 25–63 %). Twelve QTL for agronomic traits were located: a QTL for yield under infestation (bin 5.03, p = 15 %); two QTL for grain moisture (bins 1.07 and 8.05); two QTL for days to anthesis (bin 1.07 and 8.05); two QTL for days to silking (bins 8.04 and 10.02); three QTL for plant height (bins 5.04, 8.05 and 9.03); and two QTL for ear height (bins 8.05 and 9.03). No genetic correlations between yield and other traits were observed. The cross validation (CV) approach showed that the estimation biases for QTL for resistance traits were higher than those for agronomic traits. This work stresses the importance of the region 9.03 for controlling corn borer resistance and suggests the presence of QTL with small effect on ear-resistance traits. At the same genomic region, there are also genes that control plant and ear height and future works could elucidate whether these genes are the same or are closely linked. The QTL for yield seem to play an important role in MCB tolerance in this genetic background. Large biases observed for QTL effects by CV were mainly due to the small sample size used and were higher for resistance traits due to their larger genetic complexity. We consider that it is more appropriate to select for grain yield under infestation instead of selecting for resistance traits because resistance to MCB could have unfavorable associations with agronomic traits.