Precision Mapping of Quantitative Trait Loci

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.     

林木数量性状基因定位中的若干问题

随着ＤＮＡ分子标记技术的迅速发展,ＱＴＬ定位已成为当前生物学研究领域的前沿。迄今已对许多种动、植物定位了许多重要性状的ＱＴＬ。这些研究促进了遗传学的发展,并将作为育种的新策略应用。与作物相比,林木ＱＴＬ定位有其特性。本文详细讨论了林木的生物学特性对ＱＴＬ定位的影响、ＱＴＬ定位的系谱设计和统计分析方法。     

Quantitative Trait Loci Mapping of the Mouse Plasma Proteome (pQTL)

A current challenge in the era of genome-wide studies is to determine the responsible genes and mechanisms underlying newly identified loci. Screening of the plasma proteome by high-throughput mass spectrometry (MALDI-TOF MS) is considered a promising approach for identification of metabolic and disease processes. Therefore, plasma proteome screening might be particularly useful for identifying responsible genes when combined with analysis of variation in the genome. Here, we describe a proteomic quantitative trait locus (pQTL) study of plasma proteome screens in an F₂ intercross of 455 mice mapped with 177 genetic markers across the genome. A total of 69 of 176 peptides revealed significant LOD scores (≥5.35) demonstrating strong genetic regulation of distinct components of the plasma proteome. Analyses were confirmed by mechanistic studies and MALDI-TOF/TOF, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of the two strongest pQTLs: A pQTL for mass-to-charge ratio (m/z) 3494 (LOD 24.9, D11Mit151) was identified as the N-terminal 35 amino acids of hemoglobin subunit A (Hba) and caused by genetic variation in Hba. Another pQTL for m/z 8713 (LOD 36.4; D1Mit111) was caused by variation in apolipoprotein A2 (Apoa2) and cosegregated with HDL cholesterol. Taken together, we show that genome-wide plasma proteome profiling in combination with genome-wide genetic screening aids in the identification of causal genetic variants affecting abundance of plasma proteins.     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

Interval Mapping of Quantitative Trait Loci Employing Correlated Trait Complexes

An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/ a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)(2) (A/ a) >/= H(x)(2) (A/ a). The last inequality holds even if y does not depend on A/ a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H(2) (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1 - H(2)). Thus, situations could exist that from the inequality H(xy)(2) (A/ a) >/= H(x)(2) (A/ a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).     

Mapping multiple Quantitative Trait Loci by Bayesian classification

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible effects and separate classes for markers having positive or negative effects on the trait. The posterior probability of a marker's classification provides a natural statistic for evaluating credibility of identified QTL. This approach performs well, especially with a large number of markers but a relatively small sample size. A heat map to visualize the results is proposed so as to allow investigators to be more or less conservative when identifying QTL. We validated the method using a well-characterized data set for barley heading values from the North American Barley Genome Mapping Project. Application of the method to a new data set revealed sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species. A simulation study demonstrated the power of this approach across levels of trait heritability and when marker data were sparse.     

猪的基因图谱及数量性状位点定位

在人类基因组计划的带动下,猪的遗传连锁图谱和细胞遗传学图谱有了较大的进步,利用目前猪基因组图谱的研究成果,通过基因组扫描法和候选基因法,可以对猪重要经济性状的主效基因位点进行区域定位,进而图位克隆,找到主效基因,为现代遗传育种奠定理论基础。     

Multiple-Interval Mapping for Quantitative Trait Loci With a Spike in the Trait Distribution

For phenotypic distributions where many individuals share a common value—such as survival time following a pathogenic infection—a spike occurs at that common value. This spike affects quantitative trait loci (QTL) mapping methodologies and causes standard approaches to perform suboptimally. In this article, we develop a multiple-interval mapping (MIM) procedure based on mixture generalized linear models (GLIMs). An extended Bayesian information criterion (EBIC) is used for model selection. To demonstrate its utility, this new approach is compared to single-QTL models that appropriately handle the phenotypic distribution. The method is applied to data from Listeria infection as well as data from simulation studies. Compared to the single-QTL model, the findings demonstrate that the MIM procedure greatly improves the efficiency in terms of positive selection rate and false discovery rate. The method developed has been implemented using functions in R and is freely available to download and use.     

家畜抗性等级性状的QTL定位方法

在广义线性模型的框架内模拟研究了家畜抗性等级性状的QTL定位方法,QTL参数的估计采用最大似然方法,比较了阈模型方法与一般线性方法的QTL定位效率,并对影响等级性状QTL定位效率的主要因素（QTL效应、性状的遗传力）进行了模拟研究,实验设计为多个家系的女儿设计,资源群体大小为500头。研究结果表明：在QTL位置参数估计及检验功效方面,阈模型方法具有一定的优势,对抗性等级性状QTL定位的功效也高于线性方法。另外,性状遗传力和QTL效应的大小对QTL定位的准确度也有直接的影响,随着性状遗传力QTL效应的     

Shrinkage Estimation Method for Mapping Multiple Quantitative Trait Loci

In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.     

Genetic Mapping of Quantitative Trait Loci Underlying Flowering Time in Chrysanthemum (Chrysanthemum morifolium)

Flowering time is an important trait in chrysanthemum, but its genetic basis remains poorly understood. An intra-specific mapping population bred from the cross between the autumn-flowering cultivar ‘Yuhualuoying’ and the summer-flowering ‘Aoyunhanxiao’ was used to determine the number and relative effect of QTL segregating for five measures of flowering time. From flowering time data recorded over two consecutive seasons, 35 additive QTL were detected, each explaining between 5.8% and 22.7% of the overall phenotypic variance. Of these, 13 were detected in both years. Nine genomic regions harboring QTL for at least two of the five traits were identified. Ten pairs of loci epistatically determined the flowering time, but their contribution to the overall phenotypic variance was less than for the additive QTL. The results suggest that flowering time in chrysanthemum is principally governed by main effect QTL but that epistasis also contributes to the genetic architecture of the trait, and the major QTL identified herein are useful in our ongoing efforts to streamline the improvement of chrysanthemum via the use of molecular methodology.     

Mapping Quantitative Trait Loci Affecting Variation in Drosophila Triacylglycerol Storage

Objective: Recent genetic studies indicate that Drosophila melanogaster could be a powerful model to identify genes involved in mammalian adipocyte differentiation and fat storage. The objective of our study was to identify quantitative trait loci (QTLs) that contribute to variation in triacylglycerol (TAG) storage in two D. melanogaster laboratory strains. Research Methods and Procedures: We used two genetic mapping procedures to identify loci with main and epistatic effects on TAG storage. First, using 68 recombinant inbred lines derived from the unrelated Oregon R and Russian 2b strains, we mapped the location of QTLs affecting TAG storage using both composite interval mapping and Bayesian epistatic methods. Second, we used the quantitative deficiency mapping procedure to identify candidate genes affecting this trait within one of the QTLs identified on the second chromosome. For both mapping experiments, flies were cultured in standard conditions. TAG content of 4‐ to 5‐day‐old flies, adjusted for live body mass and total proteins, was used as the phenotypic measure. Results: Multiple QTLs associated with variation in TAG storage were identified by the genome‐wide recombination mapping method, and some of them were sex‐specific. The QTLs had main effects, but a male‐specific epistatic interaction between two QTLs was also found. Finally, two closely linked QTLs were detected by deficiency mapping at 57E1‐57E3 and 57E4‐57F1 on chromosome 2, the first of which causes female‐specific variation in TAG between the Oregon R and 2b strains. Discussion: Our results suggest that variation in TAG storage in D. melanogaster is controlled by different genetic mechanisms and different sets of QTLs in male and female flies.     

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by meta-analysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.YIELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yield-component traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that “fine-mapped” quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008).Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely >10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear.In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL meta-analysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described.     

猪胴体脂肪沉积性状的QTL定位

市场生猪价格的决定因素之一是背膘厚度,因此,对养猪行业来说,确认影响这一性状的基因组区域就显得非常必要,1998年,由大白和梅山猪杂交繁殖的资源家系,随机选留81头F2,全部屠宰,用48个微卫星标记进行1－4,6－7号染色体（SSC）的扫描,用线性模型最小二乘法对胴体脂肪性状进行区间作图,除SSC2外,其余5条染色体共有14个QTL影响脂肪沉积,其中SSC7上4个QTL有印记效应存在,平均背膘厚QTL定位于SSC1,4和7,SSC4平均背膘厚QTL达染色体极显著水平（P＜0．01）,SSC1和7上平均背膘厚STL与多个文献报道结果一致,可能是家猪中普遍存在的QTL,花油率QTL定位于SSC6,内脂率QTL定位于SSC7,肥肉率等其他4项脂肪性状定位在SSC3,4和7。     

Quantitative Trait Loci Mapping for ChlorophyⅡ Fluorescence and Associated Traits in Wheat （ Triticum aestivum）

Parameters of chlorophyll fluorescence kinetics （PCFKs） under drought stress condition are generally used to characterize instincts for dehydration tolerance in wheat （Triticum aestivum L.）. Therefore, it is important to map quantitative trait loci （QTLs） for PCFKs in wheat genetic improvement for drought tolerance. A doubled haploid （DH） population with 150 lines, derived from a cross between two common wheat varieties, Hanxuan 10 and Lumai 14, was used to analyze the correlation between PCFKs and chlorophyll content （CHIC） and to map QTLs at the grainfilling stage under conditions of both rainfed （drought stress, DS） and well-watered （WW）, respectively. QTLs for these traits were detected by QTLMapper version 1.0 based on the composite Interval mapping method of the mixed-linear model. The results showed a very significant positive correlation between Fv, Fm, Fv/Fm and Fv/Fo. The correlation coefficients were generally higher under WW than under DS. Also, there was a significant or a highly significant positive correlation between Fv, Fm, Fv/Fm, Fv/Fo and CHIC. The correlation coefficients were higher in the DS group than the WW group. A total of 14 additive QTLs （nine QTLs detected under DS and five QTLs under WW） and 25 pairs of eplstatlc QTLs （15 pairs detected under DS and 10 pairs under WW） for PCFKs were mapped on chromosomes 6A, 7A, 1B, 3B, 4D and 7D. The contributions of additive QTLs for PCFKs to phenotype variation were from 8.40% to 72.72%. Four additive QTLs （two QTLs detected under DS and WW apiece） controlling Chic were mapped on chromosomes 1A, 5A and 7A. The contributions of these QTLs for ChIC to phenotype variation were from 7.27% to 11.68%. Several QTL clusters were detected on chromosomes 1B, 7A and 7D, but no shared chromosomal regions for them were identified under different water regimes, indicating that these QTLs performed different expression patterns under rainfed and well-watered conditions.     

Maximum Likelihood Mapping of Quantitative Trait Loci Using Full-Sib Families

A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.     

微卫星标记与奶牛数量性状QTL定位

本文对奶牛产奶性能（产奶量、乳脂率、乳脂量、蛋白率、蛋白量）、乳房炎及体细胞数、繁殖性能、生产寿命等数量性状QTL定位研究进展进行了综述。 Abstract:Mapping of quantitative trait loci for some important traits (milk yield,fat percentage,fat yield,protein percentage,protein yield,clinical mastitis and somatic cell count,reproductive performance,productive life,etc.) in dairy cattle was introduced in this review.     

Mapping Quantitative Trait Loci for Post-Anthesis Dry Matter Accumulation in Wheat

Post-anthesis photoassimilation is very important for wheat （Triticum aestivum L.） grain filling. The aim of the present study was to map quantitative trait loci （QTL） for post-anthesis dry matter accumulation （DMA）. A set of 120 doubled haploid （DH） lines, derived from winter wheat varieties Hanxuan 10 and Lumai 14, was grown under field conditions in two consecutive growing seasons during 2002-2004 in Beijing. Post-anthesis DMA per culm and related traits, including flag leaf greenness （FLG） and flag leaf weight （FLW; dry weight per flag leaf） at flowering, and grain weight per ear （GWE） were investigated. All traits segregated continuously in the DH population in both trials. The DMA was significantly and positively correlated with GWE, with the correlation coefficients being 0.79 and 0.66 in the 2002-2003 and 2003-2004 growing seasons （both P〈0.01）, suggesting the importance of DMA in grain filling. Further correlation analysis showed that FLW was more closely correlated with DMA and GWE than FLG in both growing seasons, indicating that FLW was more important than FLG in influencing DMA and GWE. In total, 30 QTLs for these four traits were mapped and distributed on 10 chromosomes. Phenotypic variations explained by an individual QTL were in the range 5.8%-21.3%, 5.9%-17.2%, 5.1%-18.1%, and 5.6%-16.2% for FLG, FLW, DMA, and GWE, respectively. Eight QTLs for DMA were detected, of which four （on chromosome arms 2AS, 4BL, 5AS, and 7AS） were linked with QTLs for GWE; two （on chromosome arms 5BL and 7BL） coincided with QTLs for FLW. These results may provide useful information for developing marker-assisted selection for the improvement of DMA.