The imprinted gene DIO3 is a candidate gene for litter size in pigs

Genomic imprinting is an important epigenetic phenomenon, which on the phenotypic level can be detected by the difference between the two heterozygote classes of a gene. Imprinted genes are important in both the development of the placenta and the embryo, and we hypothesized that imprinted genes might be involved in female fertility traits. We therefore performed an association study for imprinted genes related to female fertility traits in two commercial pig populations. For this purpose, 309 SNPs in fifteen evolutionary conserved imprinted regions were genotyped on 689 and 1050 pigs from the two pig populations. A single SNP association study was used to detect additive, dominant and imprinting effects related to four reproduction traits; total number of piglets born, the number of piglets born alive, the total weight of the piglets born and the total weight of the piglets born alive. Several SNPs showed significant (q-value < 0.10) additive and dominant effects and one SNP showed a significant imprinting effect. The SNP with a significant imprinting effect is closely linked to DIO3, a gene involved in thyroid metabolism. The imprinting effect of this SNP explained approximately 1.6% of the phenotypic variance, which corresponded to approximately 15.5% of the additive genetic variance. In the other population, the imprinting effect of this QTL was not significant (q-value > 0.10), but had a similar effect as in the first population. The results of this study indicate a possible association between the imprinted gene DIO3 and female fertility traits in pigs.     

High-resolution association mapping of number of teats in pigs reveals regions controlling vertebral development

Background

Selection pressure on the number of teats has been applied to be able to provide enough teats for the increase in litter size in pigs. Although many QTL were reported, they cover large chromosomal regions and the functional mutations and their underlying biological mechanisms have not yet been identified. To gain a better insight in the genetic architecture of the trait number of teats, we performed a genome-wide association study by genotyping 936 Large White pigs using the Illumina PorcineSNP60 Beadchip. The analysis is based on deregressed breeding values to account for the dense family structure and a Bayesian approach for estimation of the SNP effects.

Results

The genome-wide association study resulted in 212 significant SNPs. In total, 39 QTL regions were defined including 170 SNPs on 13 Sus scrofa chromosomes (SSC) of which 5 regions on SSC7, 9, 10, 12 and 14 were highly significant. All significantly associated regions together explain 9.5% of the genetic variance where a QTL on SSC7 explains the most genetic variance (2.5%). For the five highly significant QTL regions, a search for candidate genes was performed. The most convincing candidate genes were VRTN and Prox2 on SSC7, MPP7, ARMC4, and MKX on SSC10, and vertebrae δ-EF1 on SSC12. All three QTL contain candidate genes which are known to be associated with vertebral development. In the new QTL regions on SSC9 and SSC14, no obvious candidate genes were identified.

Conclusions

Five major QTL were found at high resolution on SSC7, 9, 10, 12, and 14 of which the QTL on SSC9 and SSC14 are the first ones to be reported on these chromosomes. The significant SNPs found in this study could be used in selection to increase number of teats in pigs, so that the increasing number of live-born piglets can be nursed by the sow. This study points to common genetic mechanisms regulating number of vertebrae and number of teats.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-542) contains supplementary material, which is available to authorized users.     

A quantitative trait locus for oleic fatty acid content on Sus scrofa chromosome 7

A partial genome scan using microsatellite markers was conducted to detect quantitative trait loci (QTLs) for 10 fatty acid contents of backfat on 15 chromosomes in a porcine resource population. Two QTLs were discovered on Sus scrofa chromosome 4 (SSC4) and SSC7. The QTL on SSC4 was located between marker loci sw1336 and sw512, and this QTL was detected (P < 0.05) only for linoleic acid. Its position was in proximity of those mapped for linoleic acid content in previous studies. The QTL on SSC7 was mapped between markers swr1343 and sw2155, and it was significant (P < 0.05) only for oleic acid. A novelty of the QTL for oleic acid was suggested because the QTL was located far from any other QTLs previously mapped for fatness traits. The QTL on SSC7 explained 19% of phenotypic variation for oleic acid content. Further studies on fine mapping and positional comparative candidate gene analysis would be the next step toward better understanding of the genetic architecture of fatty acid contents.     

Quantitative trait loci mapping for fatty acid contents in the backfat on porcine chromosomes 1, 13, and 18

A partial genome scan using microsatellite markers was conducted in order to detect quantitative trait loci (QTLs) for 10 fatty acid contents of the backfat in a pig reference population. Two QTLs were found by studying SSC1, SSC13, and SSC18, where QTLs had already been identified for backfat thickness. A QTL was located between marker loci S0113 and SW974 on chromosome 1; this QTL was only significantly detected (P < 0.05) for linoleic acid. The other QTL was discovered between markers S0062 and S0120 on chromosome 18, and its significance only showed (P < 0.05) for myristic acid. The two QTLs mapped to the same location as the backfat thickness QTL. A third of the phenotypic variation was explained for linoleic acid by the QTL on chromosome 1, and a quarter for myristic acid by the QTL on chromosome 18. Further studies on fine mapping and positional comparative candidate gene analyses will be the next step toward a better understanding of the genetic architecture of fatty acid contents.     

A two‐step approach to map quantitative trait loci for meat quality in connected porcine F2 crosses considering main and epistatic effects

The aim of this study was to map QTL for meat quality traits in three connected porcine F₂ crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two‐step procedure. In the first step, the QTL were mapped using a multi‐QTL multi‐allele model that was tailored to analyse multiple connected F₂ crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.     

QTLs for days to silking in a recombinant inbred line maize population subjected to high and low nitrogen regimes

Days to silking (DTS) is one of the most important traits in maize (Zea mays). To investigate its genetic basis, a recombinant inbred line population was subjected to high and low nitrogen (N) regimes to detect quantitative trait loci (QTLs) associated with DTS. Three QTLs were identified under the high N regime; these explained 25.4% of the phenotypic variance. Due to additive effects, the QTL on chromosome 6 increased DTS up to 0.66 days; while the other two QTLs mapped on chromosome 9 (one linked with Phi061 and the other linked with Nc134) decreased DTS 0.89 and 0.91 days, respectively. Under low N regime, two QTLs were mapped on chromosomes 6 and 9, which accounted for 25.9% of the phenotypic variance. Owing to additive effects, the QTL on chromosome 6 increased DTS 0.67 days, while the other QTL on chromosome 9 decreased it 1.48 days. The QTL on chromosome 6, flanked by microsatellite markers Bnlg1600 and Phi077, was detected under both N regimes. In conclusion, we identified four QTLs, one on chromosome 6 and three on chromosome 9. These results contribute to our understanding of the genetic basis of DTS and will be useful for developing marker-assisted selection in maize breeding programs.     

A genome scan for quantitative trait loci influencing carcass,post‐natal growth and reproductive traits in commercial Angus cattle

To gain insight into the number of loci of large effect that underlie variation in cattle, a quantitative trait locus (QTL) scan for 14 economically important traits was performed in two commercial Angus populations using 390 microsatellites, 11 single nucleotide polymorphisms (SNPs) and one duplication loci. The first population comprised 1769 registered Angus bulls born between 1955 and 2003, with Expected Progeny Differences computed by the American Angus Association. The second comprised 38 half‐sib families containing 1622 steers with six post‐natal growth and carcass phenotypes. Linkage analysis was performed by half‐sib least squares regression with gridqtl or Bayesian Markov chain Monte Carlo analysis of complex pedigrees with loki . Of the 673 detected QTL, only 118 have previously been reported, reflecting both the conservative approach to QTL reporting in the literature, and the more liberal approach taken in this study. From 33 to 71% of the genetic variance and 35 to 56% of the phenotypic variance in each trait was explained by the detected QTL. To analyse the effects of 11 SNPs and one duplication locus within candidate genes on each trait, a single marker analysis was performed by fitting an additive allele substitution model in both mapping populations. There were 53 associations detected between the SNP/duplication loci and traits with ?log₁₀P_nominal≥ 4.0, where each association explained 0.92% to 4.4% of the genetic variance and 0.01% to 1.86% of the phenotypic variance. Of these associations, only six SNP/duplication loci were located within 8 cM of a QTL peak for the trait, with two being located at the QTL peak: SST_DG156121:c.362A>G for ribeye muscle area and TG_X05380:c.422C>T for calving ease. Strong associations between several SNP/duplication loci and trait variation were obtained in the absence of any detected linked QTL. However, we reject the causality of several commercialized DNA tests, including an association between TG_X05380:c.422C>T and marbling in Angus cattle.     

干旱胁迫下水稻柱头外露率加性、上位性效应和Q×E互作

在耐旱性筛选设施内对一套水稻重组自交系群体(共185个株系)进行两年的水分胁迫和非胁迫处理,调查每穗颖花数(sNP)、单边柱头外露率(PSES)、双边柱头外露率(PDES)和柱头总外露率(PES)等4个开花相关性状.方差分析结果显示年份、株系和水分处理,以及相互间互作的效应均达显著水平.表型相关以PSES和PES间最高(r=0.9752***),其次为PDES和PES (r=0.7150***),最次为PSES和PDES间(r=0.5424***).利用203个SSR标记建立的连锁图,胁迫和非胁迫条件下各检测到6个SNP的主效QTL,3～4个PSES、PDES和PES的主效QTL;检测到1～9对上位性QTL影响颖花数和柱头外露率.大部分加性和上位性效应的贡献率较低(0.76%～9.92%),仅有少数QTL或上位性QTL解释总方差的10%以上.一些主效和上位性QTL在PSES、PDES和PES间被共同检测到,解释了不同柱头外露率指标间高度正相关关系.几乎没有在水分胁迫和非胁迫两种条件下都检测到的QTL,暗示着干旱对颖花数和柱头外露率有严重的影响.     

Construction of high-density linkage map and identification of QTLs for resistance to sorghum downy mildew in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Sorghum downy mildew (SDM), caused by obligate biotrophic fungi Peronosclerospora sorghi, is an economically important disease of maize. The genetics of resistance was reported to be polygenic thereby necessitating identification of QTLs for resistance to SDM to initiate effective marker-assisted selection programs. During post-rainy and winter season of 2012, 645 F_2:3 progeny families from the cross CML153 (susceptible) × CML226 (resistant) were screened for their reaction to SDM. Characterization of QTLs affecting resistance to SDM was undertaken using the genetic linkage map with 319 polymorphic SSR and SNP marker loci and the phenotypic data of F_2:3 families. Three QTLs conferring resistance to SDM were consistently identified on chromosomes 2, 3 and 6 in both seasons. The resistant parent CML226 contributed all the QTL alleles conferring resistance to SDM. The major QTL located on chromosome 2 explained 38.68% of total phenotypic variation in the combined analysis with a LOD score of 9.12. All the three QTL showed partially dominant gene effects in combined analysis. The detection of more than one QTL supports the hypothesis that quantitative genes control resistance to P. sorghi. The generation was advanced to F₆ using markers linked to major QTLs on chromosomes 2 and 3 to derive 33 SDM resistant maize inbred lines.     

Dissection of Additive, Epistatic Effect and Q × E Interaction of Quantitative Trait Loci Influencing Stigma Exsertion Under Water Stress in Rice

Four flowering related traits, spikelet number per panicle (SNP), percentage of single exserted stigma (PSES), dual exserted stigma (PDES) and total exserted stigma (PES) of a RI population with 185 lines under water stress and non-stress conditions for 2 years, were investigated in a drought tolerance screening facility. ANOVA results showed high significance between years, lines, and water stress treatments, together with interactions among them in pairs. Highest phenotypic correlation was found between PSES and PES (r = 0.9752^***), followed by PDES and PES (r = 0.7150^***), and PSES and PDES (r = 0.5424^***). Based on a linkage map of 203 SSR markers, six main effect QTLs were detected for SNP and three or four main effect QTLs were associated with PSES, PDES and PES under stress or non-stress conditions. There were one to nine pairs of epistatic QTLs influencing SNP and stigma exsertion. The contribution rates of additive and epistatic effects seemed to be in a low magnitude for most cases (0.76%-9.92%) while a few QTLs or QTL pairs explained more than 10% of total variance. Some main effect QTL and epistasis were commonly detected among PSES, PDES and PES, explaining the high positive correlation between them. Few QTLs were detected under both water stress and non-stress condition, implying that drought had severe impact on the genetic behaviors of both spikelet number and stigma exsertion.     

Non-additive genetic variation in growth,carcass and fertility traits of beef cattle

Background

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation.

Methods

Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs.

Results

The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance.

Conclusions

Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.     

Mapping QTLs for 15 morpho-metric traits in Arabidopsis thaliana using Col-0 × Don-0 population

Genome wide quantitative trait loci (QTL) mapping was conducted in Arabidopsis thaliana using F2 mapping population (Col-0 × Don-0) and SNPs markers. A total of five linkage groups were obtained with number of SNPs varying from 45 to 59 per linkage group. The composite interval mapping detected a total of 36 QTLs for 15 traits and the number of QTLs ranged from one (root length, root dry biomass, cauline leaf width, number of internodes and internode distance) to seven (for bolting days). The range of phenotypic variance explained (PVE) and logarithm of the odds ratio of these 36 QTLs was found be 0.19–38.17% and 3.0–6.26 respectively. Further, the epistatic interaction detected one main effect QTL and four epistatic QTLs. Five major QTLs viz. Qbd.nbri.4.3, Qfd.nbri.4.2, Qrdm.nbri.5.1, Qncl.nbri.2.2, Qtd.nbri.4.1 with PVE > 15.0% might be useful for fine mapping to identify genes associated with respective traits, and also for development of specialized population through marker assisted selection. The identification of additive and dominant effect QTLs and desirable alleles of each of above mentioned traits would also be important for future research.     

Gene actions at loci underlying several quantitative traits in two elite rice hybrids

To understand the gene activities controlling nine important agronomic quantitative traits in rice, we applied a North Carolina design 3 (NC III design) analysis to recombinant inbred lines (RILs) in highly heterotic inter- (IJ) and intra-subspecific (II) hybrids by performing the following tasks: (1) investigating the relative contribution of additive, dominant, and epistatic effects for performance traits by generation means analysis and variance component estimates; (2) detecting the number, genomic positions, and genetic effects of QTL for phenotypic traits; and (3) characterizing their mode of gene action. Under an F∞-metric, generation means analysis and variance components estimates revealed that epistatic effects prevailed for the majority of traits in the two hybrids. QTL analysis identified 48 and 66 main-effect QTL (M-QTL) for nine traits in IJ and II hybrids, respectively. In IJ hybrids, 20 QTL (41.7%) showed an additive effect of gene actions, 20 (41.7%) showed partial-to-complete dominance, and 8 (16.7%) showed overdominance. In II hybrids, 34 QTL (51.5%) exhibited additive effects, 14 (21.2%) partial-to-complete dominance, and 18 (27.3%) overdominance. There were 153 digenic interactions (E-QTL) in the IJ hybrid and 252 in the II hybrid. These results suggest that additive effects, dominance, overdominance, and particularly epistasis attribute to the genetic basis of the expression of traits in the two hybrids. Additionally, we determined that the genetic causes of phenotypic traits and their heterosis are different. In the plants we studied, the phenotypic traits investigated and their heterosis were conditioned by different M-QTL and E-QTL, respectively, and were mainly due to non-allelic interactions (epistasis).     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.     

QTL mapping analysis of plant height and ear height of maize (Zea mays L.)

Genetic map containing 103 microsatellite loci obtained on 200 F2 plants derived from the cross R15 x 478 was used for quantitative trait loci (QTL) mapping in maize. QTL were characterized in a population of 200 F2:4 lines, derived from selfing the F2 plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTL affecting plant height and ear height. The QTL explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8 and 9.     

Dynamic Quantitative Trait Loci Analysis of Seed Reserve Utilization during Three Germination Stages in Rice

In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed reserve utilization during the early (day 6), middle (day 10) and late (day 14) germination stages. The seedling dry weight (SDW) and weight of the mobilized seed reserve (WMSR) were increased, while the seed reserve utilization efficiency (SRUE) decreased, during the process of seed germination. The SDW and WMSR were affected by the seed weight, while the SRUE was not affected by the seed weight. A total of twenty unconditional and twenty-one conditional additive QTLs and eight epistatic QTLs were identified at three germination stages, and the more QTLs were expressed at the late germination stage. Among them, twelve additive and three epistatic QTLs for SDW, eight additive and three epistatic QTLs for WMSR and thirteen additive and two epistatic QTLs for SRUE were identified, respectively. The phenotypic variation explained by each additive QTL, epistatic QTL and QTL × development interaction ranged from 6.10 to 23.91%, 1.79 to 6.88% and 0.22 to 2.86%, respectively. Two major additive QTLs qWMSR7.1 and qSRUE4.3 were identified, and each QTL could explain more than 20% of the total phenotypic variance. By comparing the chromosomal positions of these additive QTLs with those previously identified, eleven QTLs might represent novel genes. The best four cross combinations of each trait for the development of RIL populations were selected. The selected RILs and the identified QTLs might be applicable to improve rice seed reserve utilization by the marker-assisted selection approach.     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai’an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions (QEs). Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rht1 and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

玉米雌雄开花间隔天数、结穗率与产量的数量性状位点(QTL)分析

采用SSR标记连锁图谱和复合区间作图法在山西灌溉和干旱胁迫条件下,对玉米(Zea mays L.)自交系黄早四×掖107组合的F3群体雌雄开花间隔天数(ASI)、结穗率和籽粒产量进行了数量性状位点(QTL)定位及基因效应分析.结果表明,在两种水分处理下,ASI、结穗率与籽粒产量的相关性均达到显著水平(P＜0.05).在灌溉和干旱胁迫下,分别检测到3个和2个控制ASI的QTL,位于第1、2、3和第2、5染色体上.在灌溉条件下,在第3和第6染色体上各检测到1个控制结穗率的QTL,基因作用方式呈加性或部分显性,可解释19.9%的表型变异;在干旱条件下,在第3、 7、10染色体上共检测到4个控制结穗率的QTL,基因作用方式为显性或部分显性,可解释60.4%的表型变异.在灌溉和干旱胁迫下,控制产量的QTL分别定位在第3、6、7和第1、2、4、8染色体上,基因作用方式均以加性或部分显性为主,可解释的表型变异为7.3%～22.0%.在干旱条件下,借助连锁分子标记和基因效应分析,可构建包含ASI、结穗率和产量QTL的选择指数,用于分子标记辅助育种.     

Quantitative trait loci analysis of plant height and ear height in maize (Zea mays L.)

Genetic map containing 103 microsatellite loci obtained on 200 F₂ plants derived from the cross R15 × 478 was used for quantitative trait loci (QTL) mapping in maize. QTLs were characterized in a population of 200 F_2:4 lines, derived from selfing the F₂ plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTLs affecting plant height and ear height. The QTLs explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8, and 9. The text was submitted by the authors in English.